# Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery

Source: [Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery](<https://www.sota2.com/research/paper/68fa680d1e209131cea19d8c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:28:00.017Z

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

Published: 2020-07-22T09:35:04.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.11784>)

[PDF](<https://arxiv.org/pdf/2007.11784.pdf>)