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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.
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Affine registration of thermal images of plantar feet using convolutional neural networks
(Elsevier Ltd, 2024-09-01)
The use of a thermal camera to detect abnormal plantar foot temperature changes can be an effective way to identify the early signs of diabetic foot ulceration. In this work, we performed the affine registration of the plantar foot thermal images using four models based on convolutional neural networks. The process include two parts: an affine registration model for estimating transformation parameters and a spatial transformer for getting the registered image. The performances of the four models were evaluated using the Dice similarity coefficient (DSC), Mean Square Error (MSE), and peak signal-to-noise ratio (PSNR). In the first step, Methods were applied to register the left and right feet of the same subject, called “contralateral registration” and in the second step, the methods were evaluated on a pair of images of the same subject taken in two different times (T0 and T10) using a cold stress test protocol. Results showed that the used convolutional neural networks are robust in both types of registration (contralateral and multitemporal), and they are suitable for the targeted application, with the DSC of 95% for contralateral registration and a DSC of 92% for multitemporal registration. Furthermore, a transversal clinical study was perform on diabetic patients, that classified individuals into ischemic and non-ischemic groups. The objective was to analyze the coherence between the thermal results and medical data. The mean absolute point-to-point temperature difference |ΔT| between left and right foot is lower in non-ischemic patients than in those with ischemia, with p<0.05.
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