# Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural
Networks††thanks: © 2022 IEEE. Personal use of this material is permitted.
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Amirreza Mahbod
Rupert Ecker  Institute for Pathophysiology and Allergy Research
Medical University of Vienna
Vienna, Austria  Research and Development Department
TissueGnostics GmbH
Vienna, Austria  Gerald Schaefer
Isabella Ellinger  Department of Computer Science
Loughborough University
Loughborough, United Kingdom  Institute for Pathophysiology and Allergy
Research
Medical University of Vienna
Vienna, Austria

###### Abstract

Foot ulcer is a common complication of diabetes mellitus and, associated with
substantial morbidity and mortality, remains a major risk factor for lower leg
amputations. Extracting accurate morphological features from foot wounds is
crucial for appropriate treatment. Although visual inspection by a medical
professional is the common approach for diagnosis, this is subjective and
error-prone, and computer-aided approaches thus provide an interesting
alternative. Deep learning-based methods, and in particular convolutional
neural networks (CNNs), have shown excellent performance for various tasks in
medical image analysis including medical image segmentation.

In this paper, we propose an ensemble approach based on two encoder-decoder-
based CNN models, namely LinkNet and U-Net, to perform foot ulcer
segmentation. To deal with a limited number of available training samples, we
use pre-trained weights (EfficientNetB1 for the LinkNet model and
EfficientNetB2 for the U-Net model) and perform further pre-training using the
Medetec dataset while also applying a number of morphological-based and
colour-based augmentation techniques. To boost the segmentation performance,
we incorporate five-fold cross-validation, test time augmentation and result
fusion.

Applied on the publicly available chronic wound dataset and the MICCAI 2021
Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-
art performance with data-based Dice scores of 92.07% and 88.80%,
respectively, and is the top ranked method in the FUSeg challenge leaderboard.
The Dockerised guidelines, inference codes and saved trained models are
publicly available at https://github.com/masih4/Foot_Ulcer_Segmentation.

###### Index Terms:

Foot ulcer, medical image analysis, image segmentation, machine learning, deep
learning, ensemble.

##  I Introduction

Diabetes mellitus is one of the most common chronic diseases with an
increasing rate of prevalence over the past decades [1]. It can cause several
complications for the patient such as cardiovascular disease, retinopathy and
neuropathy [2]. A serious medical condition associated with diabetes mellitus
are skin ulcers on the foot, which can lead to amputation [3]. Up to 3% of
patients with diabetes mellitus have an active foot ulcer, while lifetime risk
of developing a foot ulcer is up to 25%. Different treatments may be applied
based on the ulcer type and its morphological appearance [4].

For appropriate treatment, lesion characteristics such as length, width, area,
and volume need to be measured. Moreover, the wound area is a useful predictor
of the final outcome and allows to monitor the healing process and thus to
evaluate the effect of treatment [5]. Visual inspection and measurement of
foot ulcers by a medical expert is the common approach to investigate these
morphological features. However, this is subjective and error-prone [6]. A
promising alternative is to acquire images of the area and apply computer-
aided methods to segment the ulcer. Following automated wound segmentation,
the morphological features such as lesion width or area can then be easily
extracted from the segmentation masks using morphological operations.

In the literature, a number of computerised methods have been proposed to
perform foot ulcer segmentation. These include approaches ranging from
classical computer vision techniques to state-of-the-art machine learning and
deep learning models. Clustering, edge detection, adaptive thresholding, and
region growing are examples of conventional image analysis approaches for
wound segmentation [7, 8], while, based on hand-crafted features, classifiers
such as multi-layer perceptrons and support vector machine can also be trained
for foot ulcer segmentation [7, 9]. Similar to other medical image
segmentation tasks, deep learning and convolutional neural network (CNN)-based
approaches have been shown to outperform other approaches for foot ulcer
segmentation [10]. CNN-based architectures employed for this task include
fully convolutional neural networks (FCNs), U-Net, and Mask R-CNN [10, 11,
12].

Figure 1: Workflow of the proposed method.

In this paper, inspired by our former work on other medical image segmentation
tasks [13, 14], we propose and develop a model based on two well-established
encoder-decoder-based CNNs, namely U-Net and LinkNet, to segment wounds in
clinical foot images. We use pre-trained CNNs, 5-fold cross-validation, test
time augmentation (TTA), and result fusion to boost the segmentation
performance. We evaluate our method on the publicly available chronic wound
dataset [10] and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge
dataset and achieve state-of-the-art segmentation performance for both cases
while achieving first rank for the latter111Challenge leaderboard (accessed on
2022-05-18): https://uwm-bigdata.github.io/wound-segmentation/.

##  II Method

The generic workflow of our proposed method, for both training and testing
phases, is shown in Fig. 1.

###  II-A Datasets

We use the following publicly available datasets:

  * •

Medetec dataset [15]: consists of 152 clinical images, with corresponding
segmentation masks, of several types of open foot wounds. The images are zero-
padded and have a fixed size of $224\times 224$ pixels [10]. We use this
dataset only for pre-training of the employed segmentation models.

  * •

Chronic wound dataset [10]: contains 1010 clinical images from 889 patients,
with the corresponding segmentation masks of all images also available. To
obtain a fixed size for all images, a YOLOV3 object detection model [16] is
used to localise and crop the wounds inside the images. For non-square images,
zero-padding is used to yield a fixed size of $512\times 512$ pixels for the
entire dataset. Of the 1010 images, 810 images are used for training and the
remaining 200 kept aside for testing as suggested in [10].

  * •

FUSeg dataset [17]: this dataset is an extended version of the chronic wound
dataset and is the dataset used as the training and testing sets of the MICCAI
2021 Foot Ulcer Segmentation Challenge. It contains 1210 images where 1010
images (identical to the chronic wound dataset) are provided for training and
200 images are used for evaluation. The ground truth segmentation masks of the
200 test images are kept private by the challenge organisers and are used only
for evaluation purposes. Similar to the chronic wound dataset, all images have
a fixed size of $512\times 512$ pixels. Further details about this dataset can
be found on the challenge platform222https://fusc.grand-challenge.org/ and in
[17].

Fig. 2 shows some example images from the datasets.

Figure 2: Image samples from the Medetec dataset (left) and the chronic wound
dataset/FuSeg dataset (right).

###  II-B CNN models

In our approach, we employ two CNN models, namely U-Net [18] and LinkNet [19].
Although other encoder-decoder-based CNNs such as U-Net++ [20] or attention
U-Net [21] can also be used, the chosen CNN models have shown excellent
segmentation performance for a variety of medical image segmentation tasks
while being computability less expensive [13, 14, 22]. For both models, we use
pre-trained CNNs in the decoder part of the model, for LinkNet a pre-trained
EfficientNetB1 [23], and for U-Net a pre-trained EfficientNetB2 [23].
EfficientNet-based models are state-of-the-art classification models employed
in various medical image classification tasks such as skin lesion
classification or diabetic retinopathy detection [24, 25]. We therefore employ
them as the backbone of our encoder-decoder-based segmentation models. In
particular, we use two variants of the EfficientNet model, B1 and B2, since
one is originally designed for smaller images (and hence more suitable for
smaller lesions), while the other is designed for slightly larger images (and
hence more suitable for larger lesions) [23].

###  II-C Training

As shown in Fig. 1, we use the Medetec dataset as a task-specific dataset for
further pre-training. During training, we use random scaling (scale limit 0.1
with 0.3 probability), random 90-degree rotations (with 0.5 probability),
vertical and horizontal flipping (with 0.5 probability), as well as brightness
and contrast shifts (limit of 0.15 with 0.4 probability) as augmentation
techniques. We use the full-size images and train each model for 80 epochs
with a learning rate (LR) scheduler that reduces the LR by 90% after every 25
epochs, and an initial LR of 0.001. We use a batch size of 4 and employ the
Adam optimiser [26]. As loss function, we use a combination of Dice loss and
focal loss (with equal weights). For each dataset, five-fold cross-validation
is exploited, and the best models based on segmentation scores on the
validation sets are saved to be used in the inference phase.

TABLE I: Segmentation results on the chronic wound data set obtained by methods reported in [10] (top) in comparison to our approach (bottom). |  | image-based
---
Dice [%]
precision [%] | recall [%] |  | data-based
---
IoU [%]
| data-based
---
Dice [%]
VGG16 | n/a | 83.91 | 78.35 | n/a | 81.03
SegNet | n/a | 83.66 | 86.49 | n/a | 85.05
U-Net | n/a | 89.04 | 91.29 | n/a | 90.15
Mask-RCNN | n/a | 94.30 | 86.40 | n/a | 90.20
MobileNetV2 | n/a | 90.86 | 89.76 | n/a | 90.30
MobileNetV2 + CCL | n/a | 91.01 | 89.97 | n/a | 90.47
LinkNet-EffB1 | 83.93 | 92.88 | 91.33 | 85.35 | 92.09
U-Net-EffB2 | 84.09 | 92.23 | 91.57 | 85.01 | 91.90
Ensemble | 84.42 | 92.68 | 91.80 | 85.51 | 92.07
TABLE II: Results of ablation study to show the effectiveness of five-fold cross-validation (CV) and test time augmentation (TTA) on the segmentation scores. The best result for each evaluation measure for each of the models is bolded. | CV | TTA |  | image-based
---
Dice [%]
precision [%] | recall [%] |  | data-based
---
IoU [%]
| data-based
---
Dice [%]
LinkNet-EffB1 | ✗ | ✗ | 82.98 | 91.92 | 90.39 | 83.37 | 91.15
LinkNet-EffB1 | ✗ | ✓ | 82.98 | 92.38 | 90.83 | 84.50 | 91.60
LinkNet-EffB1 | ✓ | ✓ | 83.93 | 92.88 | 91.33 | 85.35 | 92.09
U-Net-EffB2 | ✗ | ✗ | 83.07 | 91.73 | 89.98 | 83.23 | 90.84
U-Net-EffB2 | ✗ | ✓ | 83.31 | 92.74 | 90.33 | 84.37 | 91.52
U-Net-EffB2 | ✓ | ✓ | 84.09 | 92.23 | 91.57 | 85.01 | 91.90

###  II-D Ensemble

To boost segmentation performance, we use three distinct ensembling
strategies, namely five-fold cross-validation, test time augmentation and
result fusion.

As illustrated in Fig. 3, instead of using the entire training set to train a
single model, we divide the training data into 5 partitions and train one sub-
model for each cross-validation split (i.e., for each of the sub-models, we
use 4 partitions for training and the hold-out partition for validation). In
the inference phase, we pass a test image to all 5 sub-models and take the
average over the obtained results. We perform five-fold cross-validation for
both U-Net and Link-Net models, and hence in total, 10 trained sub-models are
generated.

Figure 3: Five-fold cross-validation training scheme. The entire training data
is divided into 5 partitions. For each sub-model, 4 partitions are used for
training, while the hold-out partition is used for validation. The generated
sub-models are then used in the inference phase during ensembling.

As shown in former work on various image segmentation/classification tasks
[27, 28], test time augmentation (TTA) can boost the overall performance. We
therefore employ TTA in the inference phase to yield improved segmentation.
For this, we use 0, 90, 180, and 270-degree rotations as well as horizontal
flipping.

Last not least, since we train two distinct models – a LinkNet with an
EfficientNetB1 backbone and a U-Net with an EfficientNetB2 backbone – we fuse
their results in the inference phase. Various methods such as voting, bagging,
or averaging exist for results fusion. We use straight-forward averaging to
fuse the prediction probability masks from the two models. Averaging has been
shown to yield comparable fusion performance to other more sophisticated
ensembling methods for various medical image analysis tasks [22, 27].

###  II-E Post-processing

To form the final segmentation masks for a test image, we first binarise the
fused prediction probability vectors using a 0.5 threshold. We further apply
two post-processing steps, namely filling holes and removing very small
detected objects, with the identical settings as described in [10].

###  II-F Evaluation

To evaluate the segmentation performance of our proposed method and also to
compare the results to other state-of-the-art models, we use precision (Equ.
1), recall (Equ. 2), and data-based Dice score (Equ. 3) as suggested in [10].
For the chronic wound dataset, we also report the data-based intersection over
union (IoU) (Equ. 4) score and the image-based Dice score (Equ. 5) for our
proposed method. For the FUSeg challenge, we report the data-based Dice score,
precision and recall as these scores were provided by the challenge organisers
(the data-based Dice score was used as the main evaluation index in the
challenge) [17].

Based on the standard definitions of true positives ($TP$), false positives
($FP$), false negatives ($FN$), and with $N$ the total number of test images,
precision is defined as

$Precison=\frac{\sum_{i=1}^{N}TP_{i}}{\sum_{i=1}^{N}TP_{i}+\sum_{i=1}^{N}FP_{i}},$
(1)

recall as

$Recall=\frac{\sum_{i=1}^{N}TP_{i}}{\sum_{i=1}^{N}TP_{i}+\sum_{i=1}^{N}FN_{i}},$
(2)

the data-based Dice score as

$Dice_{data}=\frac{\sum_{i=1}^{N}2TP_{i}}{\sum_{i=1}^{N}2TP_{i}+\sum_{i=1}^{N}FP_{i}+\sum_{i=1}^{N}FN_{i}},$
(3)

the data-based IoU as

$IoU_{data}=\frac{\sum_{i=1}^{N}TP_{i}}{\sum_{i=1}^{N}TP_{i}+\sum_{i=1}^{N}FP_{i}+\sum_{i=1}^{N}FN_{i}},$
(4)

and the image-based Dice score as

$Dice_{image}=\frac{1}{N}\sum_{i=1}^{N}\frac{2TP_{i}}{2TP_{i}+FP_{i}+FN_{i}}.$
(5)

TABLE III: Top five performers of the MICCAI 2021 FUSeg challenge. team | approach | precision [%] | recall [%] |  | data-based
---
Dice [%]
Mahbod et al. | this paper | 91.55 | 86.22 | 88.80
Zhang et al. | U-Net with HarDNet68 | 88.87 | 86.31 | 87.57
Galdran et al. [29] | Stacked U-Nets | 90.03 | 84.00 | 86.91
Hong et al. | n/a | n/a | n/a | 86.27
Qayyum et al. | U-Net with ASPP | n/a | n/a | 82.29
Figure 4: Examples from the chronic wound test set with acceptable
segmentation performance. Yellow lines show the manual ground truth, and blue
lines the predicted segmentation masks.  Figure 5: Examples from the chronic
wound test set with poor segmentation performance (Dice scores below 60%). The
yellow lines show the manual ground truth in the images, and the blue lines
show the predicted segmentation masks. Figure 6: Examples from the chronic
wound test set with a zero Dice score. The yellow lines show the manual ground
truth in the images, and the blue lines show the predicted segmentation masks.

##  III Results and Discussion

We report the results for the test set of the chronic wound dataset in Table
I. There, we also compare our proposed method with a number of deep learning-
based approaches reported in [10]. For our method, we give the results of the
LinkNet model (with EfficientNetB1 backbone) and the U-Net model (with
EfficientNetB2 backbone) on their own as well as the results of the fused
model.

As we can see, even on their own, the LinkNet and U-Net models outperform the
other state-of-the-art methods for most evaluation indexes (all except
precision). It is equally evident that the final fusion scheme yields another
small improvement and thus excellent segmentation performance in comparison to
the other state-of-the-art models. Moreover, comparing the U-Net results with
the U-Net-EffB2 results shows the advantage of using a pre-trained CNN in the
encoder part of the segmentation model.

To show the effectiveness of the utilised ensemble strategies, we perform an
ablation study and report the results in Table II. As the results for each of
the models show, both the use of five-fold cross-validation and TTA results in
better segmentation performance.

As can be inferred from Table I, our proposed method delivers excellent
segmentation performance for most of the images, and we show some examples
with acceptable segmentation scores in Fig. 4. However, for 18 of the 200 test
images, relatively poor Dice scores below 60% are obtained, where in 10 cases
the segmentation performance is very poor with a zero Dice score. In these
latter cases, there is either no lesion in the image but a false positive area
is predicted (3 cases), or there are very small lesions in the images that are
missed by the algorithm (6 cases), while the remaining image has an incorrect
manual annotation. Some examples with poor (Dice score between 0% and 60%) and
very poor segmentation results (Dice score of 0%) are shown in Fig. 5 and Fig.
6, respectively.

With the presented methodology, we attended the MICCAI 2021 FUSeg challenge
and submitted our inference codes and saved models in the frame of a
Dockerised container. The results from our approach and the top four
participating teams of the challenge are reported in Table III. These results
were directly calculated by the challenge organisers and are available in the
challenge leaderboard333https://uwm-bigdata.github.io/wound-segmentation/. As
the challenge remains open for new post submissions, the ranking may change in
future.. As we can observe from Table III, our method outperforms the other
approaches and is thus top ranked in the challenge based on the data-based
Dice score. Our method also achieves the best precision performance and a very
competitive recall score compared to the other approaches.

##  IV Conclusions

Computer-aided foot ulcer segmentation can provide an efficient and effective
alternative to manual analysis and subsequently calculated wound area
measures. In this paper, we have proposed a powerful deep learning-based
technique to segment foot ulcers in clinical images. Our approach incorporates
two CNN segmentation networks and various ensembling strategies for improved
segmentation performance. Results on two benchmark datasets demonstrate our
method to yield excellent segmentation performance and to outperform other
state-of-the-art deep learning-based algorithms, while being top-ranked in the
recent MICCAI 2021 FUSeg challenge.

## Acknowledgements

This project was supported by the Austrian Research Promotion Agency (FFG),
No. 872636. We would like to also thank NVIDIA for their generous GPU
donation. This research study was conducted retrospectively using human
subject data made available in open access form in former studies. Ethical
approval was not required as confirmed by the license attached with the open
access data.

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