27 February 2018 Segmentation of skin lesions in chronic graft versus host disease photographs with fully convolutional networks
Author Affiliations +
Chronic graft-versus-host disease (cGVHD) is a frequent and potentially life-threatening complication of allogeneic hematopoietic stem cell transplantation (HCT) and commonly affects the skin, resulting in distressing patient morbidity. The percentage of involved body surface area (BSA) is commonly used for diagnosing and scoring the severity of cGVHD. However, the segmentation of the involved BSA from patient whole body serial photography is challenging because (1) it is difficult to design traditional segmentation method that rely on hand crafted features as the appearance of cGVHD lesions can be drastically different from patient to patient; (2) to the best of our knowledge, currently there is no publicavailable labelled image set of cGVHD skin for training deep networks to segment the involved BSA. In this preliminary study we create a small labelled image set of skin cGVHD, and we explore the possibility to use a fully convolutional neural network (FCN) to segment the skin lesion in the images. We use a commercial stereoscopic Vectra H1 camera (Canfield Scientific) to acquire ~400 3D photographs of 17 cGVHD patients aged between 22 and 72. A rotational data augmentation process is then applied, which rotates the 3D photos through 10 predefined angles, producing one 2D projection image at each position. This results in ~4000 2D images that constitute our cGVHD image set. A FCN model is trained and tested using our images. We show that our method achieves encouraging results for segmenting cGVHD skin lesion in photographic images.
Conference Presentation
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Jianing Wang, Jianing Wang, Fuyao Chen, Fuyao Chen, Laura E. Dellalana, Laura E. Dellalana, Madan H. Jagasia, Madan H. Jagasia, Eric R. Tkaczyk, Eric R. Tkaczyk, Benoit M. Dawant, Benoit M. Dawant, "Segmentation of skin lesions in chronic graft versus host disease photographs with fully convolutional networks", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750N (27 February 2018); doi: 10.1117/12.2293334; https://doi.org/10.1117/12.2293334

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