{"entry_id": "a3064917-528d-4466-b925-e1a53e43faf0", "infos": [{"txt": "SACNet:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 0, "bbox": [[0.151, 0.062, 0.293, 0.098]]}]}, "tags": ["title"], "label": "article_title", "web_segment_id": 9, "global_sentence_id": 0, "edu_l1_label": "BOT"}, {"txt": " A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 0, "bbox": [[0.293, 0.062, 0.849, 0.098], [0.086, 0.097, 0.914, 0.134]]}]}, "tags": ["title"], "label": "article_title", "web_segment_id": 9, "global_sentence_id": 1, "edu_l1_label": "BOT"}, {"txt": "Lin Zhang, Wenbo Gao, Jie Yi, Yunyun Yang∗", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 100000, "bbox": [[0.327, 0.152, 0.672, 0.169]]}]}, "tags": ["text"], "label": "author", 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"23s058005@stu.hit.edu.cn, 23B358001@stu.hit.edu.cn, tomatosquid@163.com, yangyunyun@hit.edu.cn", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 300000, "bbox": [[0.154, 0.216, 0.846, 0.231]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 16, "global_sentence_id": 6, "edu_l1_label": "EDU_O"}, {"txt": "7", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 400000, "bbox": [[0.031, 0.275, 0.051, 0.284]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 21, "global_sentence_id": 7, "edu_l1_label": "EDU_O"}, {"txt": "20", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 400000, "bbox": [[0.03, 0.284, 0.056, 0.303]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 23, "global_sentence_id": 8, "edu_l1_label": "EDU_O"}, {"txt": "201", "language": "english", "position": {"pdf_position": 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"global_sentence_id": 12, "edu_l1_label": "EDU_O"}, {"txt": "Abstract—Multi-organ segmentation in medical image analysisis crucial for diagnosis and treatment planning.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.096, 0.26, 0.49, 0.274], [0.08, 0.272, 0.386, 0.287]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 13, "edu_l1_label": "BOS"}, {"txt": "However, many factors complicate the task, including variability in different target categories and interference from complex backgrounds.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.386, 0.272, 0.49, 0.287], [0.08, 0.285, 0.49, 0.3], [0.08, 0.297, 0.49, 0.312]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 14, "edu_l1_label": "IOS"}, {"txt": "In this paper, we utilize the knowledge of Deformable Convolution V3 (DCNv3) and multi-object segmentation to optimize our Spatially Adaptive Convolution Network (SACNet) in three aspects: feature extraction, model architecture, and loss constraint, simultaneously enhancing the perception of different segmentation targets.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.08, 0.31, 0.49, 0.325], [0.08, 0.323, 0.49, 0.337], [0.08, 0.335, 0.49, 0.35], [0.08, 0.348, 0.49, 0.363], [0.08, 0.36, 0.49, 0.375], [0.08, 0.373, 0.214, 0.388]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 15, "edu_l1_label": "IOS"}, {"txt": "Firstly, we propose the Adaptive Receptive Field Module (ARFM), which combines DCNv3 with a series of customized block-level and architecture-level designs similar to transformers.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.214, 0.373, 0.49, 0.388], [0.08, 0.386, 0.49, 0.4], [0.08, 0.398, 0.49, 0.413], [0.08, 0.411, 0.185, 0.425]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 16, "edu_l1_label": "IOS"}, {"txt": "This module can capture the unique features of different organs by adaptively adjusting the receptive feld according to various targets.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.185, 0.411, 0.49, 0.425], [0.08, 0.423, 0.49, 0.438], [0.08, 0.436, 0.274, 0.451]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 17, "edu_l1_label": "IOS"}, {"txt": "Secondly, we utilize ARFM as building blocks to construct the encoder-decoder of SACNet and partially share parameters between the encoder and decoder, making the network wider rather than deeper.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.274, 0.436, 0.49, 0.451], [0.08, 0.448, 0.49, 0.463], [0.08, 0.461, 0.49, 0.476], [0.08, 0.474, 0.404, 0.488]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 18, "edu_l1_label": "IOS"}, {"txt": "This design achieves a shared lightweight decoder and a more parametereffcient and effective framework.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.404, 0.474, 0.49, 0.488], [0.08, 0.486, 0.49, 0.501], [0.08, 0.499, 0.302, 0.513]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 19, "edu_l1_label": "IOS"}, {"txt": "Lastly, we propose a novel continuity dynamic adjustment loss function, based on t-vMF dice loss and cross-entropy loss, to better balance easy and complex classes in segmentation.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.302, 0.499, 0.49, 0.513], [0.08, 0.511, 0.49, 0.526], [0.08, 0.524, 0.49, 0.539], [0.08, 0.536, 0.233, 0.551]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 20, "edu_l1_label": "IOS"}, {"txt": "Experiments on 3D slice datasets from Synapse demonstrate that SACNet delivers superior segmentation performance in multi-organ segmentation tasks compared to several existing methods.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.233, 0.536, 0.49, 0.551], [0.08, 0.549, 0.49, 0.564], [0.08, 0.562, 0.49, 0.576], [0.08, 0.574, 0.239, 0.589]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 21, "edu_l1_label": "IOS"}, {"txt": "Index Terms—Multi-organ segmentation, Adaptive spatial ag-gregation, Category balancing", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.096, 0.587, 0.49, 0.602], [0.08, 0.6, 0.273, 0.614]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 10, "global_sentence_id": 22, "edu_l1_label": "IOS"}, {"txt": "I.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 600000, "bbox": [[0.222, 0.628, 0.232, 0.643]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 12, "global_sentence_id": 23, "edu_l1_label": "BOS"}, {"txt": "INTRODUCTION", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 600000, "bbox": [[0.232, 0.628, 0.349, 0.642]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 12, "global_sentence_id": 24, "edu_l1_label": "IOS"}, {"txt": "Multi-organ medical image segmentation, which simultaneously provides clearer visualization of anatomical and pathological structures across multiple organs, signifcantly enhances diagnostic effciency and accuracy [1]- [2].", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.096, 0.648, 0.49, 0.664], [0.08, 0.663, 0.49, 0.679], [0.08, 0.679, 0.49, 0.694], [0.08, 0.694, 0.433, 0.709]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 25, "edu_l1_label": "IOS"}, {"txt": "However, it remains challenging due to several diffculties:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.076, 0.71, 0.494, 0.862]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 26, "edu_l1_label": "IOS"}, {"txt": " (1) Variability across target categories.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.076, 0.71, 0.494, 0.862]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 27, "edu_l1_label": "IOS"}, {"txt": "Not only do different organs within the same dataset vary signifcantly in size, shape, and texture, but even different slices of the same organ can exhibit considerable differences.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.076, 0.71, 0.494, 0.862]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 28, "edu_l1_label": "IOS"}, {"txt": "This variability makes it challenging for segmentation models to generalize effectively, as they may overft to specifc features seen during training and struggle with unseen morphological variations.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.076, 0.71, 0.494, 0.862]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 29, "edu_l1_label": "IOS"}, {"txt": "(2) Interference from complex backgrounds.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.076, 0.71, 0.494, 0.862]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 30, "edu_l1_label": "IOS"}, {"txt": "The complexity of surrounding anatomical structures and the presence of", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.076, 0.71, 0.494, 0.862]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 31, "edu_l1_label": "IOS"}, {"txt": "varying textures, intensities, and overlapping tissues introduce substantial challenges.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.51, 0.259, 0.92, 0.274], [0.51, 0.274, 0.659, 0.289]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 32, "edu_l1_label": "IOS"}, {"txt": "These intricate backgrounds can lead to ambiguous boundaries and obscure the precise contours of target organs, compromising the model’s ability to accuratelysegment organs and often resulting in incomplete or inaccurate segmentation outcomes.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.659, 0.274, 0.92, 0.289], [0.51, 0.289, 0.92, 0.304], [0.51, 0.304, 0.92, 0.319], [0.51, 0.319, 0.92, 0.335], [0.51, 0.334, 0.669, 0.35]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 33, "edu_l1_label": "IOS"}, {"txt": "To address these challenges in multi-organ medical image segmentation, recent studies [3]- [16] have employed various methods, including standard, dilated, and large-kernel convolutions, transformers, and hybrid architectures, to improve segmentation accuracy.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 900000, "bbox": [[0.526, 0.351, 0.92, 0.366], [0.51, 0.366, 0.92, 0.381], [0.51, 0.381, 0.92, 0.397], [0.51, 0.396, 0.92, 0.412], [0.51, 0.412, 0.663, 0.427]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 34, "edu_l1_label": "IOS"}, {"txt": "Dilated and large-kernel convolutions expand the receptive feld, while transformers [9] utilize selfattention to capture long-range dependencies.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 900000, "bbox": [[0.663, 0.412, 0.92, 0.427], [0.51, 0.427, 0.92, 0.442], [0.51, 0.442, 0.813, 0.457]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 35, "edu_l1_label": "IOS"}, {"txt": "However, these models often lack adaptability to the specifc structures of different organs, leading to potential segmentation inaccuracies.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 900000, "bbox": [[0.813, 0.442, 0.92, 0.457], [0.51, 0.457, 0.92, 0.472], [0.51, 0.472, 0.92, 0.487]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 36, "edu_l1_label": "IOS"}, {"txt": "Some researchers [17]- [20] have incorporated deformable convolutions and deformable attention transformers to address these issues, though challenges such as sampling fexibility, computational complexity, and data requirements remain.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 900000, "bbox": [[0.51, 0.487, 0.92, 0.502], [0.51, 0.502, 0.92, 0.517], [0.51, 0.517, 0.92, 0.532], [0.51, 0.532, 0.89, 0.547]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 37, "edu_l1_label": "IOS"}, {"txt": "Additionally, enhancements to the Dice loss function [21]- [26] have been proposed to handle segmentation imbalances, but limitations persist.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 900000, "bbox": [[0.89, 0.532, 0.92, 0.547], [0.51, 0.547, 0.92, 0.563], [0.51, 0.563, 0.92, 0.578], [0.51, 0.578, 0.632, 0.593]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 38, "edu_l1_label": "IOS"}, {"txt": "We propose the SACNet framework, which features an Adaptive Receptive Field Module (ARFM), a widenet strategy, and a dynamic continuity adjustment loss function:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.526, 0.594, 0.92, 0.609], [0.51, 0.609, 0.92, 0.625], [0.51, 0.625, 0.852, 0.64]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 7, "global_sentence_id": 39, "edu_l1_label": "IOS"}, {"txt": "• ARFM:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1100000, "bbox": [[0.526, 0.65, 0.596, 0.661]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 6, "global_sentence_id": 40, "edu_l1_label": "IOS"}, {"txt": " To handle varying target categories and complexbackgrounds, we combine DCNv3 with grouped convolutions, ensuring each segmentation target has an adaptive receptive feld.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1100000, "bbox": [[0.596, 0.65, 0.92, 0.661], [0.542, 0.661, 0.92, 0.676], [0.542, 0.676, 0.92, 0.691], [0.542, 0.691, 0.642, 0.706]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 6, "global_sentence_id": 41, "edu_l1_label": "IOS"}, {"txt": "Additional elements like Feed-Forward, Layer Scale, and DropPath blocks enhance stability and geometric perception.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 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["text"], "label": "content", "web_segment_id": 8, "global_sentence_id": 44, "edu_l1_label": "IOS"}, {"txt": "• Continuity Dynamic Adjustment Loss Function (CT-Loss):", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.526, 0.801, 0.92, 0.812], [0.542, 0.811, 0.584, 0.827]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 45, "edu_l1_label": "IOS"}, {"txt": " Combining t-vMF Dice loss and cross-entropy loss, CTLoss adjusts based on the Intersection Over Union (IOU) after each epoch, handling imbalanced data by dynamically adjusting the similarities for different classes.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.584, 0.811, 0.92, 0.827], [0.542, 0.827, 0.92, 0.842], [0.542, 0.842, 0.92, 0.857], [0.542, 0.857, 0.92, 0.872], [0.542, 0.872, 0.592, 0.887]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 46, "edu_l1_label": "IOS"}, {"txt": "Our method, SACNet, outperforms existing approaches in", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1400000, "bbox": [[0.526, 0.893, 0.92, 0.908]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 11, "global_sentence_id": 47, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.078, 0.872, 0.492, 0.908]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 48, "edu_l1_label": "EDU_O"}, {"txt": "multi-organ segmentation tasks, as demonstrated by state-ofthe-art results and thorough ablation studies.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.08, 0.063, 0.49, 0.078], [0.08, 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{"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2000000, "bbox": [[0.272, 0.443, 0.49, 0.458], [0.08, 0.458, 0.49, 0.473], [0.08, 0.473, 0.49, 0.488], [0.08, 0.488, 0.338, 0.503]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 56, "edu_l1_label": "IOS"}, {"txt": "The input image, sized 3×224×224, begins with a Stem Layer for initial processing.The encoder consists of four stages with embedding sizes of 112, 224, 448, and 896, each containing a series of ARFM modules and down-sampling layers.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2000000, "bbox": [[0.338, 0.488, 0.49, 0.503], [0.08, 0.503, 0.49, 0.519], [0.08, 0.518, 0.49, 0.534], [0.08, 0.534, 0.49, 0.549], [0.08, 0.549, 0.333, 0.564]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 57, "edu_l1_label": "IOS"}, {"txt": "Each ARFM module includes fve blocks: DCNv3 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maintain gradient fow.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2100000, "bbox": [[0.14, 0.639, 0.49, 0.654], [0.08, 0.654, 0.14, 0.669]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 60, "edu_l1_label": "IOS"}, {"txt": "The encoder produces four feature maps of size H are linked to the decoder via skip connections.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2100000, "bbox": [[0.14, 0.654, 0.49, 0.669], [0.212, 0.669, 0.223, 0.678], [0.08, 0.684, 0.402, 0.7]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 61, "edu_l1_label": "IOS"}, {"txt": "The module sequence is illustrated in Fig.1.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2100000, "bbox": [[0.402, 0.684, 0.49, 0.7], [0.08, 0.7, 0.289, 0.715]]}]}, 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"content", "web_segment_id": 4, "global_sentence_id": 65, "edu_l1_label": "IOS"}, {"txt": "Each block starts with layer normalization and upsamples the input by a factor of two to match the encoder outputs.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2300000, "bbox": [[0.248, 0.757, 0.49, 0.772], [0.08, 0.772, 0.49, 0.787], [0.08, 0.787, 0.188, 0.802]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 66, "edu_l1_label": "IOS"}, {"txt": "The ARFM module refnes features, followed by layer normalization, maintaining the same dimensions.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2300000, "bbox": [[0.188, 0.787, 0.49, 0.802], [0.08, 0.802, 0.49, 0.818]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 67, "edu_l1_label": "IOS"}, {"txt": "The outputs are concatenated with the encoder outputs via skip connections.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2300000, "bbox": [[0.08, 0.818, 0.49, 0.833], [0.08, 0.833, 0.199, 0.848]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 68, "edu_l1_label": "IOS"}, {"txt": "To handle the growing complexity due to DCNv3’s sampling points, we apply the WideNet [31]strategy, sharing DCNv3 projection weights between encoder and decoder to reduce parameter load.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2300000, "bbox": [[0.199, 0.833, 0.49, 0.848], [0.08, 0.848, 0.49, 0.863], [0.08, 0.863, 0.49, 0.878], [0.08, 0.878, 0.344, 0.893]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 69, "edu_l1_label": "IOS"}, {"txt": "This allows SACNet to expand in width while limiting depth.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2300000, "bbox": [[0.344, 0.878, 0.49, 0.893], [0.08, 0.893, 0.361, 0.908]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 70, "edu_l1_label": "IOS"}, {"txt": "After upsampling,", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2300000, "bbox": [[0.361, 0.893, 0.49, 0.908]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 71, "edu_l1_label": "IOS"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2400000, "bbox": [[0.509, 0.061, 0.923, 0.186]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 8, "global_sentence_id": 72, "edu_l1_label": "EDU_O"}, {"txt": "Fig. 2: Qualitative comparisons of the segmentation performance of our approach alongside other segmentation methods. The frst two rows display Synapse dataset results with color codes: yellow (aorta), green (gallbladder), orange (left kidney), cyan (right kidney), plum (liver), purple (pancreas), indigo (spleen), and crimson (stomach).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.507, 0.19, 0.922, 0.283]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 7, "global_sentence_id": 73, "edu_l1_label": "EDU_O"}, {"txt": "images are resized to H × W × N (where N is the numberof classes) for fnal segmentation, followed by Softmax for pixel classifcation.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.51, 0.309, 0.92, 0.324], [0.51, 0.324, 0.92, 0.339], [0.51, 0.339, 0.635, 0.354]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 74, "edu_l1_label": "IOS"}, {"txt": "The bottleneck block functions similarly to other decoder blocks but focuses more on channel information without altering image size.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.635, 0.339, 0.92, 0.354], [0.51, 0.354, 0.92, 0.37], [0.51, 0.369, 0.705, 0.385]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 75, "edu_l1_label": "IOS"}, {"txt": "No additional techniques like residual connections [29] or deep supervision [30] are used in the decoder.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.705, 0.369, 0.92, 0.385], [0.51, 0.385, 0.92, 0.4], [0.51, 0.4, 0.608, 0.415]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 76, "edu_l1_label": "IOS"}, {"txt": "C. Training Loss", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2600000, "bbox": [[0.51, 0.423, 0.624, 0.438]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 11, "global_sentence_id": 77, "edu_l1_label": "IOS"}, {"txt": "In this study, we tested most of the proposed loss functions for multi-class medical image segmentation and comprehensively considered the loss aggregation strategy.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2700000, "bbox": [[0.526, 0.442, 0.92, 0.457], [0.51, 0.457, 0.92, 0.472], [0.51, 0.472, 0.832, 0.488]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 78, "edu_l1_label": "IOS"}, {"txt": "We propose a novel Continuity Dynamic Adjustment Loss Function (CTLoss) based on t-vMF Dice loss [25] and the cross-entropy loss function.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2700000, "bbox": [[0.832, 0.472, 0.92, 0.488], [0.51, 0.488, 0.92, 0.503], [0.51, 0.503, 0.92, 0.518], [0.51, 0.518, 0.601, 0.533]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 79, "edu_l1_label": "IOS"}, {"txt": "This new loss function adaptively uses more compact similarities for easy classes and wider similarities for diffcult classes, making it more effective in helping the model converge better and more effciently for multi-object image segmentation.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2700000, "bbox": [[0.601, 0.518, 0.92, 0.533], [0.51, 0.533, 0.92, 0.548], [0.51, 0.548, 0.92, 0.563], [0.51, 0.563, 0.92, 0.578], [0.51, 0.578, 0.646, 0.593]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 80, "edu_l1_label": "IOS"}, {"txt": "The t-vMF Dice Loss is formulated as:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2800000, "bbox": [[0.526, 0.591, 0.789, 0.606]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 10, "global_sentence_id": 81, "edu_l1_label": "IOS"}, {"txt": "\\operatorname { c o s } \\theta _ { i } = \\sum _ { i = 1 } ^ { n } A _ { i n } B _ { i n } .", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.641, 0.607, 0.778, 0.643]]}]}, "tags": ["equation"], "label": "formula", "web_segment_id": 23, "global_sentence_id": 82, "edu_l1_label": "IOS"}, {"txt": "6", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3000000, "bbox": [[0.69, 0.687, 0.697, 0.694]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 21, "global_sentence_id": 83, "edu_l1_label": "EDU_O"}, {"txt": "1,", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3000000, "bbox": [[0.813, 0.661, 0.829, 0.67]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 22, "global_sentence_id": 84, "edu_l1_label": "IOS"}, {"txt": "(2)", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3000000, "bbox": [[0.903, 0.657, 0.92, 0.671]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 17, "global_sentence_id": 85, "edu_l1_label": "IOS"}, {"txt": "56", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3100000, "bbox": [[0.664, 0.7, 0.691, 0.718]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 19, "global_sentence_id": 86, "edu_l1_label": "EDU_O"}, {"txt": "(1 - Φπ(cosθε;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3100000, "bbox": [[0.702, 0.695, 0.833, 0.712]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 20, "global_sentence_id": 87, "edu_l1_label": "IOS"}, {"txt": "π))2", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3100000, "bbox": [[0.702, 0.695, 0.833, 0.712]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 20, "global_sentence_id": 88, "edu_l1_label": "IOS"}, {"txt": "(3)", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3100000, "bbox": [[0.903, 0.697, 0.92, 0.71]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 16, "global_sentence_id": 89, "edu_l1_label": "IOS"}, {"txt": "2=1", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3100000, "bbox": [[0.684, 0.712, 0.703, 0.721]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 18, "global_sentence_id": 90, "edu_l1_label": "IOS"}, {"txt": "where n indicates the number of class samples, i represents the category index, Ain indicates the vectors containing all positive examples predicted by the model, and Bin indicates the vectors containing all positive examples of the ground truth in the dataset.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.516, 0.727, 0.92, 0.742], [0.51, 0.742, 0.92, 0.757], [0.51, 0.757, 0.92, 0.772], [0.51, 0.772, 0.92, 0.787], [0.51, 0.787, 0.609, 0.802]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 91, "edu_l1_label": "IOS"}, {"txt": "κ is a concentration parameter that adjuststhe shape of the similarity function, and C is the number of classes.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.609, 0.787, 0.92, 0.802], [0.51, 0.802, 0.92, 0.818], [0.51, 0.818, 0.58, 0.833]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 92, "edu_l1_label": "IOS"}, {"txt": "We propose an effective algorithm that uses the Intersection Over Union (IOU) of the model on the validation set to update κ in the t-vMF Dice loss after each epoch.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.58, 0.818, 0.92, 0.833], [0.51, 0.833, 0.92, 0.848], [0.51, 0.848, 0.886, 0.863]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 93, "edu_l1_label": "IOS"}, {"txt": "Thisapproach adaptively determines κ to achieve more compactsimilarities for easy classes and wider similarities for diffcult classes.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.886, 0.848, 0.92, 0.863], [0.51, 0.863, 0.92, 0.878], [0.51, 0.878, 0.92, 0.893], [0.51, 0.893, 0.559, 0.908]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 94, "edu_l1_label": "IOS"}, {"txt": "TABLE I: Comparative analysis of model performance with 2D methods in multi-organ segmentation using the synapse dataset. The best result within each column is highlighted in bold, and the second-best is underlined. Models marked with an asterisk (∗) were implemented by us.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3300000, "bbox": [[0.078, 0.058, 0.921, 0.104]]}]}, "tags": ["table_caption"], "label": "table_title", "web_segment_id": 7, "global_sentence_id": 95, "edu_l1_label": "EDU_O"}, {"txt": "table", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3400000, "bbox": [[0.089, 0.116, 0.914, 0.278]]}]}, "tags": ["table"], "label": "table", "web_segment_id": 2, "global_sentence_id": 96, "edu_l1_label": "EDU_O"}, {"txt": "TABLE II: Comparison of Ablation Studies: Only a single parameter is altered while keeping the others constant to observe the impact of each parameter on performance. S represents the Layer Scale and DropPath block.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3500000, "bbox": [[0.078, 0.295, 0.916, 0.327]]}]}, "tags": ["table_caption"], "label": "table_title", "web_segment_id": 0, "global_sentence_id": 97, "edu_l1_label": "EDU_O"}, {"txt": "table", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3600000, "bbox": [[0.085, 0.339, 0.915, 0.427]]}]}, "tags": ["table"], "label": "table", "web_segment_id": 6, "global_sentence_id": 98, "edu_l1_label": "EDU_O"}, {"txt": "The fnal loss function combines the adaptive t-vMF Dice Loss and the cross-entropy loss:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3700000, "bbox": [[0.096, 0.459, 0.49, 0.474], [0.08, 0.474, 0.295, 0.489]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 9, "global_sentence_id": 99, "edu_l1_label": "IOS"}, {"txt": "Lct = √Lt-vmf + (1 - 7)LcE,", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3800000, "bbox": [[0.17, 0.497, 0.398, 0.513]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 17, "global_sentence_id": 100, "edu_l1_label": "IOS"}, {"txt": "(4)", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3800000, "bbox": [[0.471, 0.497, 0.49, 0.512]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 16, "global_sentence_id": 101, "edu_l1_label": "IOS"}, {"txt": "where LCE is the cross-entropy loss function, γ is the weightcoeffcient, with γ = 0.6 and 1 −γ = 0.4 representing theweights for the adaptive t-vMF Dice loss and the cross-entropy loss, respectively.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3900000, "bbox": [[0.08, 0.52, 0.49, 0.535], [0.08, 0.535, 0.49, 0.55], [0.08, 0.55, 0.49, 0.565], [0.08, 0.565, 0.196, 0.58]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 8, "global_sentence_id": 102, "edu_l1_label": "IOS"}, {"txt": "III.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4000000, "bbox": [[0.223, 0.587, 0.246, 0.603]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 14, "global_sentence_id": 103, "edu_l1_label": "BOS"}, {"txt": "EXPERIMENT", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4000000, "bbox": [[0.246, 0.587, 0.347, 0.602]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 14, "global_sentence_id": 104, "edu_l1_label": "IOS"}, {"txt": "A. Dataset and Evaluation", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4100000, "bbox": [[0.08, 0.606, 0.26, 0.621]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 11, "global_sentence_id": 105, "edu_l1_label": "IOS"}, {"txt": "The Synapse dataset consists of 30 scans of eight abdominal organs from the MICCAI 2015 Multi-Atlas Abdominal Labeling Challenge.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.096, 0.625, 0.49, 0.64], [0.08, 0.64, 0.49, 0.655], [0.08, 0.655, 0.175, 0.671]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 106, "edu_l1_label": "IOS"}, {"txt": "These eight organs include the left kidney, right kidney, aorta, spleen, gallbladder, liver, stomach, and pancreas.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.175, 0.655, 0.49, 0.671], [0.08, 0.67, 0.49, 0.686]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 107, "edu_l1_label": "IOS"}, {"txt": "We report the mean Dice Similarity Coeffcient (DSC) and the 95% Hausdorff Distance (HD95) for the segmentation of abdominal organs using 18 training cases (2212 axial slices) and 12 validation cases.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.08, 0.686, 0.49, 0.701], [0.08, 0.701, 0.49, 0.716], [0.08, 0.716, 0.49, 0.731], [0.08, 0.731, 0.24, 0.746]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 108, "edu_l1_label": "IOS"}, {"txt": "B. Setups", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.08, 0.753, 0.145, 0.768]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 10, "global_sentence_id": 109, "edu_l1_label": "IOS"}, {"txt": "The model and experiments were implemented using PyTorch 2.2.0 and the CUDA toolkit 12.1, with all experiments conducted on an NVIDIA 3090 24G GPU.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4400000, "bbox": [[0.096, 0.772, 0.49, 0.787], [0.08, 0.787, 0.49, 0.802], [0.08, 0.802, 0.369, 0.818]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 110, "edu_l1_label": "IOS"}, {"txt": "To improve computational effciency, the resolution of all training, validation, and test images was resized to 224×224.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4400000, "bbox": [[0.369, 0.802, 0.49, 0.818], [0.08, 0.818, 0.49, 0.833], [0.08, 0.833, 0.365, 0.848]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 111, "edu_l1_label": "IOS"}, {"txt": "We used a batchsize of 18, an initial learning rate of 3 × 10−4, and trainedthe models for 400 epochs.For better model initialization, we initialized the model parameters with weights pre-trained on ImageNet-1k.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4400000, "bbox": [[0.365, 0.833, 0.49, 0.848], [0.08, 0.848, 0.49, 0.863], [0.08, 0.863, 0.49, 0.878], [0.08, 0.878, 0.49, 0.893], [0.08, 0.893, 0.17, 0.908]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 112, "edu_l1_label": "IOS"}, {"txt": "IV.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4500000, "bbox": [[0.673, 0.459, 0.694, 0.474]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 15, "global_sentence_id": 113, "edu_l1_label": "BOS"}, {"txt": "RESULT", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4500000, "bbox": [[0.694, 0.459, 0.757, 0.473]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 15, "global_sentence_id": 114, "edu_l1_label": "IOS"}, {"txt": "A.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4600000, "bbox": [[0.51, 0.48, 0.524, 0.495]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 12, "global_sentence_id": 115, "edu_l1_label": "IOS"}, {"txt": "Performance on Synapse Dataset", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4600000, "bbox": [[0.532, 0.48, 0.753, 0.495]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 12, "global_sentence_id": 116, "edu_l1_label": "IOS"}, {"txt": "On the Synapse dataset, as shown in Table I, our SACNet stands out with the highest scores for most organs, achieving 89.36% for the left kidney, 85.41% for the right kidney, 95.98% for the liver, and 87.64% for the stomach.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.526, 0.5, 0.92, 0.515], [0.51, 0.515, 0.92, 0.53], [0.51, 0.53, 0.92, 0.545], [0.51, 0.545, 0.852, 0.56]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 117, "edu_l1_label": "IOS"}, {"txt": "For other organs, except the spleen, our SACNet also achieves the second-best scores.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.852, 0.545, 0.92, 0.56], [0.51, 0.56, 0.92, 0.575], [0.51, 0.575, 0.64, 0.59]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 118, "edu_l1_label": "IOS"}, {"txt": "Additionally, our DSC reaches 84.92% and HD95 is 15.13, both being the best results compared to other benchmarks.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.64, 0.575, 0.92, 0.59], [0.51, 0.59, 0.92, 0.605], [0.51, 0.605, 0.633, 0.621]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 119, "edu_l1_label": "IOS"}, {"txt": "Figure 2 provides visual examples of the segmentation outputs produced by our proposed model and other state-of-the-art methods.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.633, 0.605, 0.92, 0.621], [0.51, 0.62, 0.92, 0.636], [0.51, 0.636, 0.708, 0.651]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 120, "edu_l1_label": "IOS"}, {"txt": "As shown, our predictions have clearer boundaries between different segmentation targets: the boundaries between the pancreas and spleen are clear and smooth.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.708, 0.636, 0.92, 0.651], [0.51, 0.651, 0.92, 0.666], [0.51, 0.666, 0.92, 0.681], [0.51, 0.681, 0.593, 0.696]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 121, "edu_l1_label": "IOS"}, {"txt": "Our model is more accurate for small objects: Swin-UNet and AgileFormer miss the gallbladder in their segmentations, and TransUNet only partially segments it, but our model segments it completely.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.593, 0.681, 0.92, 0.696], [0.51, 0.696, 0.92, 0.711], [0.51, 0.711, 0.92, 0.726], [0.51, 0.726, 0.742, 0.741]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 122, "edu_l1_label": "IOS"}, {"txt": "As shown in the last two rows of Table I, replacing CDLoss with CTLoss signifcantly improves the results for DSC and HD95, as well as for most organs, particularly the left and right kidneys.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.742, 0.726, 0.92, 0.741], [0.51, 0.741, 0.92, 0.756], [0.51, 0.756, 0.92, 0.772], [0.51, 0.771, 0.816, 0.787]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 123, "edu_l1_label": "IOS"}, {"txt": "B. Ablation Study", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4800000, "bbox": [[0.51, 0.797, 0.63, 0.812]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 13, "global_sentence_id": 124, "edu_l1_label": "IOS"}, {"txt": "Using the Synapse dataset, we ablated SACNet’s key com-ponents to validate their effectiveness, as detailed in Table II.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.526, 0.818, 0.92, 0.833], [0.51, 0.833, 0.92, 0.848], [0.51, 0.848, 0.525, 0.863]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 125, "edu_l1_label": "IOS"}, {"txt": "We evaluated the impact of removing pre-trained DCNv3, FFN, Layer Scale, and DropPath blocks.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.525, 0.848, 0.92, 0.863], [0.51, 0.863, 0.796, 0.878]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 126, "edu_l1_label": "IOS"}, {"txt": "Without DCNv3, DSC dropped from 84.92% to 76.99%, and HD95 rose from 15.13 to 31.05, indicating its crucial role.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.796, 0.863, 0.92, 0.878], [0.51, 0.878, 0.92, 0.893], [0.51, 0.893, 0.79, 0.908]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 127, "edu_l1_label": "IOS"}, {"txt": "We also compared", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.79, 0.893, 0.92, 0.908]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 128, "edu_l1_label": "IOS"}, {"txt": "two loss functions:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.08, 0.063, 0.207, 0.078]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 129, "edu_l1_label": "IOS"}, {"txt": " a combination of cross-entropy and Dice loss versus our proposed CTLoss, with the latter showing superior results.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.207, 0.063, 0.49, 0.078], [0.08, 0.078, 0.49, 0.093], [0.08, 0.093, 0.187, 0.108]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 130, "edu_l1_label": "IOS"}, {"txt": "These comparisons confrm that our ARFM design, integrating key components, enhances accuracy, positioning SACNet as a state-of-the-art model for 2D multi-organ segmentation.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.187, 0.093, 0.49, 0.108], [0.08, 0.108, 0.49, 0.123], [0.08, 0.123, 0.49, 0.138], [0.08, 0.138, 0.171, 0.153]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 131, "edu_l1_label": "IOS"}, {"txt": "V. CONCLUSION", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5000000, "bbox": [[0.227, 0.171, 0.344, 0.186]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 4, "global_sentence_id": 132, "edu_l1_label": "BOS"}, {"txt": "In this study, we focus on the 2D multi-organ medical segmentation task and utilize the knowledge of deformable convolution v3 and multi-object segmentation to optimize our SACNet in three aspects:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.096, 0.197, 0.49, 0.212], [0.08, 0.212, 0.49, 0.227], [0.08, 0.227, 0.49, 0.242], [0.08, 0.242, 0.252, 0.257]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 133, "edu_l1_label": "IOS"}, {"txt": " feature extraction, model architecture, and loss constraint, simultaneously enhancing perception for different segmentation targets.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.252, 0.242, 0.49, 0.257], [0.08, 0.257, 0.49, 0.272], [0.08, 0.272, 0.308, 0.287]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 134, "edu_l1_label": "IOS"}, {"txt": "Our method is verifed on 3D slice datasets from Synapse, and the results show that our method provides better accuracy in multi-organ segmentation tasks compared to several existing methods.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.308, 0.272, 0.49, 0.287], [0.08, 0.287, 0.49, 0.302], [0.08, 0.302, 0.49, 0.318], [0.08, 0.317, 0.374, 0.333]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 135, "edu_l1_label": "IOS"}, {"txt": "REFERENCES", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5200000, "bbox": [[0.24, 0.35, 0.331, 0.365]]}]}, "tags": ["title"], "label": "reference", "web_segment_id": 5, "global_sentence_id": 136, "edu_l1_label": "EDU_O"}, {"txt": "[1] R. 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# SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation
## I.INTRODUCTION
## II.METHOD
### A. Encoder
### B.Decoder
### C. Training Loss
## III.EXPERIMENT
### A. Dataset and Evaluation
### B. Setups
## IV.RESULT
### A.Performance on Synapse Dataset
### B. Ablation Study
## V. CONCLUSION
