Identification of Melanoma From Hyperspectral Pathology Image Using 3D Convolutional Networks

• Published in: IEEE transactions on medical imaging 2021-01, Vol.40 (1), p.218-227
• Main Authors: Wang, Qian, Sun, Li, Wang, Yan, Zhou, Mei, Hu, Menghan, Chen, Jiangang, Wen, Ying, Li, Qingli
• Format: Article
• Language: English
• Subjects: Biopsy; Convolution; Diagnosis; Hyperspectral imaging; Image enhancement; Image processing; Image segmentation; Melanoma; Microscopy; Model accuracy; optical imaging; Pathology; quantification and estimation; segmentation; Sensitivity enhancement; Skin; Three-dimensional displays; Two dimensional displays; Two dimensional models
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2020.3024923

Skin biopsy histopathological analysis is one of the primary methods used for pathologists to assess the presence and deterioration of melanoma in clinical. A comprehensive and reliable pathological analysis is the result of correctly segmented melanoma and its interaction with benign tissues, and therefore providing accurate therapy. In this study, we applied the deep convolution network on the hyperspectral pathology images to perform the segmentation of melanoma. To make the best use of spectral properties of three dimensional hyperspectral data, we proposed a 3D fully convolutional network named Hyper-net to segment melanoma from hyperspectral pathology images. In order to enhance the sensitivity of the model, we made a specific modification to the loss function with caution of false negative in diagnosis. The performance of Hyper-net surpassed the 2D model with the accuracy over 92%. The false negative rate decreased by nearly 66% using Hyper-net with the modified loss function. These findings demonstrated the ability of the Hyper-net for assisting pathologists in diagnosis of melanoma based on hyperspectral pathology images.