Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery

• URL: http://arxiv.org/abs/2007.11784v1
• Date: Wed, 22 Jul 2020 09:35:04 GMT
• Title: Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery
• Authors: Siang-Ruei Wu, Hao-Yun Chang, Florence T Su, Heng-Chun Liao, Wanju Tseng, Chun-Chih Liao, Feipei Lai, Feng-Ming Hsu, Furen Xiao
• Abstract summary: We benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. In particular, we compared the model performances with respect to their sampling method, model architecture, and the choice of loss functions.
• Score: 0.8431877864777444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation of medical images with deep learning models is rapidly developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset, demonstrating the strengths and weaknesses of these algorithms in a fairly practical scenario. In particular, we compared the model performances with respect to their sampling method, model architecture, and the choice of loss functions, identifying the suitable settings for their applications and shedding light on the possible improvements.

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