Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images

Automated skin lesion segmentation is essential to assist doctors in diagnosis. Most methods focus on lesion segmentation of dermoscopy images, while a few focus on clinical images. Nearly all the existing methods tackle the binary segmentation problem as to distinguish lesion parts from normal skin parts, and are designed for diseases with localized solitary skin lesion. Besides, the characteristics of both the dermoscopy images and the clinical images are four-fold: (1) Only one skin lesion exists in the image. (2) The skin lesion mostly appears in the center of the image. (3) The backgrounds are similar between different images of same modality. (4) The resolution of images isn’t high, with an average of about \(1500\times 1200\) in several popular datasets. In contrast, this paper focuses on a four-class segmentation task for Cutaneous T-cell lymphomas (CTCL), an extremely aggressive skin disease with three visually similar kinds of lesions. For the first time, we collect a new dataset, which only contains clinical images captured from different body areas of human. The main characteristics of these images differ from all the existing images in four aspects: (1) Multiple skin lesion parts exist in each image. (2) The skin lesion parts are widely scattered in different areas of the image. (3) The background of the images has a large variety. (4) All the images have high resolutions, with an average of \(3255 \times 2535\). According to the characteristics and difficulties of CTCL, we design a new Multi Knowledge Learning Network (MKLN). The experimental results demonstrate the superiority of our method, which meet the clinical needs.