Analyse d'images thermiques de la voûte plantaire : Application au diagnostic du pied diabétique

We want to develop a mobile, real-time, and user-friendly application to detect hyperthermia for diabetic foot patients. Thermal images have been taken with the non-constraining STANDUP protocol, freehandedly, with a smartphone equipped with a FlirOne Pro thermal camera. We suggest three approaches to segment the plantar foot thermal images: blind methods, prior shape snakes, Deep Learning-based semantic segmentation methods. We have developed a new prior shape-based snake method that integrates all the prior information. We have studied four of the most popular Deep Learning segmentation networks, namely, FCN, SegNet, U-Net, and DeepLab. The DeepLab segmentation network gave the best DSC similarity coefficient (0.97) of all the tested methods and exhibited good abilities to segment difficult images. For this, we selected it for our mobile application. The Android application is operational and real-time (11 seconds with a Huawei P30 Pro). Besides, it can detect hyperthermia. A first clinical study took place in the diabetes department of the National Hospital Dos de Mayo (HNDM), in Peru. We have noticed that the temperature for at-risk patients is higher than that for low-risk ones. The second study took place in the diabetic foot service of the regional hospital of Orleans. The thermal information correlates with the general condition of the ulcer. We have also seen that our technology can detect a hidden wound. A user-friendly system for clinical or at-home care to detect and follow diabetic foot ulcers is now available.Nous souhaitons développer une application mobile, temps réel et conviviale pour détecter les hyperthermies de la voûte plantaire chez les patients diabétiques. Les images sont acquises avec le protocole STANDUP, à main levée, sans aucun système d’occultation, avec un smartphone équipé d’une caméra thermique FlirOne Pro. La principale difficulté consiste à segmenter les images thermiques. Nous proposons 3 pistes à suivre : les méthodes aveugles, celles fondées sur les snakes avec a priori de forme, la dernière basée sur l’apprentissage profond (Deep Learning). Nous avons développé une nouvelle méthode de snake avec a priori de forme qui intègre l’ensemble des informations dont nous disposons. Nous avons étudié quatre des réseaux de segmentation Deep Learning les plus populaires, à savoir, FCN, SegNet, U-Net et DeepLab. Le réseau de segmentation DeepLab a donné le meilleur coefficient de similarité DSC (0.97) de toutes les méthodes testées et a présenté de bonnes aptitudes pour segmenter des images difficiles. Pour cela, nous l’avons sélectionné pour notre application mobile de détection d’hyperthermie dans le cadre du pied diabétique. Le démonstrateur réalisé sous Android est opérationnel et temps réel (11 secondes avec un Huawei P30 Pro). De plus il détecte effectivement les hyperthermies. Une première étude clinique a eu lieu au sein du service de diabétologie de l'Hôpital National Dos de Mayo (HNDM), au Pérou. Nous avons remarqué que la température pour des personnes à risque est plus élevée que celle pour les patients à risque faible. La deuxième étude a eu lieu au sein du service de pied diabétique de l'Hôpital régional d'Orléans. L'information thermique est corrélée à l’état général de l’ulcère. On a aussi vu que notre technologie permet de détecter des plaies cachées. Les résultats obtenus dans ce travail ouvrent la voie d’un système convivial, efficace et bon marché de la mesure de température de la voûte plantaire pour un usage clinique ou à la maison dans le cadre du pied diabétique

Segmentation of Plantar Foot Thermal Images Using Prior Information

Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is added to the snake of Kass et al. in order to force its curvature to match that of the prior shape, which has a known form. Second, we defined the initial contour as the downsized prior-shape contour, which is placed inside the plantar foot surface in a vertical orientation. This choice makes the snake avoid strong false boundaries present outside the plantar region when evolving. As a result, the snake produces a smooth contour that rapidly converges to the true boundaries of the foot. The proposed method is compared to two classical prior-shape snake methods, that of Ahmed et al. and that of Chen et al. A database of 50 plantar foot thermal images was processed. The results show that the proposed method outperforms the previous two methods with a root-mean-square error of 5.12 pixels and a dice similarity coefficient of 94%. The segmentation of the plantar foot regions in the thermal images helped us to assess the point-to-point temperature differences between the two feet in order to detect hyperthermia regions. The presence of such regions is the pre-sign of ulcers in the diabetic foot. Furthermore, our method was applied to hyperthermia detection to illustrate the promising potential of thermography in the case of the diabetic foot. Associated with a friendly acquisition protocol, the proposed segmentation method is the first step for a future mobile smartphone-based plantar foot thermal analysis for diabetic foot patients

Plantar foot thermal images analysis : Application to diabetic foot diagnosis

Nous souhaitons développer une application mobile, temps réel et conviviale pour détecter les hyperthermies de la voûte plantaire chez les patients diabétiques. Les images sont acquises avec le protocole STANDUP, à main levée, sans aucun système d’occultation, avec un smartphone équipé d’une caméra thermique FlirOne Pro. La principale difficulté consiste à segmenter les images thermiques. Nous proposons 3 pistes à suivre : les méthodes aveugles, celles fondées sur les snakes avec a priori de forme, la dernière basée sur l’apprentissage profond (Deep Learning). Nous avons développé une nouvelle méthode de snake avec a priori de forme qui intègre l’ensemble des informations dont nous disposons. Nous avons étudié quatre des réseaux de segmentation Deep Learning les plus populaires, à savoir, FCN, SegNet, U-Net et DeepLab. Le réseau de segmentation DeepLab a donné le meilleur coefficient de similarité DSC (0.97) de toutes les méthodes testées et a présenté de bonnes aptitudes pour segmenter des images difficiles. Pour cela, nous l’avons sélectionné pour notre application mobile de détection d’hyperthermie dans le cadre du pied diabétique. Le démonstrateur réalisé sous Android est opérationnel et temps réel (11 secondes avec un Huawei P30 Pro). De plus il détecte effectivement les hyperthermies. Une première étude clinique a eu lieu au sein du service de diabétologie de l'Hôpital National Dos de Mayo (HNDM), au Pérou. Nous avons remarqué que la température pour des personnes à risque est plus élevée que celle pour les patients à risque faible. La deuxième étude a eu lieu au sein du service de pied diabétique de l'Hôpital régional d'Orléans. L'information thermique est corrélée à l’état général de l’ulcère. On a aussi vu que notre technologie permet de détecter des plaies cachées. Les résultats obtenus dans ce travail ouvrent la voie d’un système convivial, efficace et bon marché de la mesure de température de la voûte plantaire pour un usage clinique ou à la maison dans le cadre du pied diabétique.We want to develop a mobile, real-time, and user-friendly application to detect hyperthermia for diabetic foot patients. Thermal images have been taken with the non-constraining STANDUP protocol, freehandedly, with a smartphone equipped with a FlirOne Pro thermal camera. We suggest three approaches to segment the plantar foot thermal images: blind methods, prior shape snakes, Deep Learning-based semantic segmentation methods. We have developed a new prior shape-based snake method that integrates all the prior information. We have studied four of the most popular Deep Learning segmentation networks, namely, FCN, SegNet, U-Net, and DeepLab. The DeepLab segmentation network gave the best DSC similarity coefficient (0.97) of all the tested methods and exhibited good abilities to segment difficult images. For this, we selected it for our mobile application. The Android application is operational and real-time (11 seconds with a Huawei P30 Pro). Besides, it can detect hyperthermia. A first clinical study took place in the diabetes department of the National Hospital Dos de Mayo (HNDM), in Peru. We have noticed that the temperature for at-risk patients is higher than that for low-risk ones. The second study took place in the diabetic foot service of the regional hospital of Orleans. The thermal information correlates with the general condition of the ulcer. We have also seen that our technology can detect a hidden wound. A user-friendly system for clinical or at-home care to detect and follow diabetic foot ulcers is now available

A joint snake and atlas-based segmentation of plantar foot thermal images

International audienc

A joint snake and atlas-based segmentation of plantar foot thermal images

International audienc

A joint snake and atlas-based segmentation of plantar foot thermal images

International audienc

Segmentation of Plantar Foot Thermal Images Using Prior Information

Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is added to the snake of Kass et al. in order to force its curvature to match that of the prior shape, which has a known form. Second, we defined the initial contour as the downsized prior-shape contour, which is placed inside the plantar foot surface in a vertical orientation. This choice makes the snake avoid strong false boundaries present outside the plantar region when evolving. As a result, the snake produces a smooth contour that rapidly converges to the true boundaries of the foot. The proposed method is compared to two classical prior-shape snake methods, that of Ahmed et al. and that of Chen et al. A database of 50 plantar foot thermal images was processed. The results show that the proposed method outperforms the previous two methods with a root-mean-square error of 5.12 pixels and a dice similarity coefficient of 94%. The segmentation of the plantar foot regions in the thermal images helped us to assess the point-to-point temperature differences between the two feet in order to detect hyperthermia regions. The presence of such regions is the pre-sign of ulcers in the diabetic foot. Furthermore, our method was applied to hyperthermia detection to illustrate the promising potential of thermography in the case of the diabetic foot. Associated with a friendly acquisition protocol, the proposed segmentation method is the first step for a future mobile smartphone-based plantar foot thermal analysis for diabetic foot patients