The Challenges of Applying Deep Learning for Hemangioma Lesion Segmentation

Abstract

Infantile Hemangiomas (IH) make up the most common type of benign vascular tumors affecting children. They can grow for several months until beginning to involute. In present-day clinical practice there's no objective monitoring protocol. For more objective measures, an automatic evaluation system (CAD system) is needed to aid clinicians in assessing the effectiveness of a given patient's response to a treatment. One of the stages of these systems is the lesion segmentation. This work addresses the automatic segmentation of lesions in IH. Acknowledging that the methods in the literature for IH lesion segmentation lag behind the state-of-the-art in the image segmentation community, we conduct a comparison of various methodologies for the segmentation of the IH, including both shallow and deep methodologies. Acknowledging the lack of data in the field for a robust learning of deep models, we also evaluate transfer learning techniques to benefit from knowledge extracted in other skin lesions. The best results were obtained with the shortest path method and a multiscale convolutional neural network that merges two pipelines working at different scales. Although promising, the results put in evidence the need for better databases, collected under suitable acquisition protocols.