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Item type:Publication, Multi-view data augmentation to improve wound segmentation on 3D surface model by deep learning(Institute of Electrical and Electronics Engineers Inc., 2021-01-01)Wound area segmentation really progressed with the emergence of deep learning, due to its robustness in uncontrolled lighting and no need to design hand-crafted features but two limits have still to be overcome: firstly, its performance relies on the size and quality of the training dataset in the medical field, where data annotation is costly and time-consuming; secondly the accuracy of the segmentation depends highly on the camera distance and angle and moreover perspective effects prevent measuring real surfaces in single views. To address concurrently these two issues, we propose to apply multi-view modeling: an image sequence is acquired around the wound site and enables wound 3D reconstruction. Then, a segmentation step is run to extract roughly the wound from the background in each view and to select the best view with an original strategy. This view provides the most accurate segmentation and the real wound bed area even on non planar wounds. Finally, this segmentation is backprojected in each view to generate a complete set of well annotated real images to reinforce the learning step of the neural network. In our experiments, we compare several strategies to select the best view in the image sequence. The proposed method, tested on a dataset of 270 images, outperforms standard deep learning approach based on a single view, as recorded with DICE index and IoU score which rise respectively from 36.53% to 86.3% and 29.48% to 77.09% for the wound class to achieve an overall DICE and IoU score of 93.04% and 86.61% including background class. These results attest to the robustness of our method and its improved accuracy in the wound segmentation task. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Can deep learning wound segmentation algorithms developed for a dataset be effective for another dataset? A specific focus on diabetic foot ulcers(Institute of Electrical and Electronics Engineers Inc., 2024-01-01)Diabetic foot ulcers (DFU) represent a severe complication, often resulting from poor glycemic control, neuropathy, peripheral vascular disease, or inadequate foot care. DFUs can lead to significant morbidity, including amputation and, in severe cases, can be fatal. Recently, advancements in computer vision technologies based on artificial intelligence (AI) have shown promise in DFU management. Particularly deep learning (DL) models such as U-Net and other models and techniques, were utilized to enhance wound segmentation accuracy. This research focuses on evaluating the generalization capabilities of DL models across different DFU datasets. Specifically, we investigated whether models trained on one dataset can be effective when utilized on another dataset, addressing the challenge of cross-dataset generalization. We employed 7 popular DL models, U-Net-VGG16, U-Net-EfficientNetV2S, ABANet, Ma-Net, LinkNet, DeepLabV3+, and Segment Anything Model (SAM), with 2 DFU datasets: FUSeg challenge and DFUC challenge. A total of 54 experiments were conducted plus 27 for SAM, involving training on one dataset, and testing on another, as well as training and testing on combined datasets. The results indicate substantial variability in segmentation performance when models trained on one dataset are tested on another, highlighting the influence of dataset characteristics on model generalization. The study underscores the importance of using diverse and comprehensive datasets to develop robust DL models for DFU segmentation and its generalization. This research contributes to the understanding of DL model performance in medical image segmentation and emphasizes the need for standardized datasets in improving DFU management through computer vision.