Related papers: Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery

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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