# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 5 Conclusions  
We benchmarked five commonly used deep learning segmentation models on our SRS dataset. We confirmed that these approaches also work on a heterogeneous dataset, but with decreased performance. We discovered that the V-Net architecture worked best for this specific task. With the top dice scores, the smallest size of the model, and the shortest inference time, V-Net may be a good choice to improve upon. We also found that when training on the dataset with such heterogeneity and class imbalance, using weighted cross-entropy loss with log-soft-dice term significantly improved the performance. **Supplementary Materials:** GitHub repository, [[https://github.com/raywu0123/Brain-Tumor-Segmentation](https://github.com/raywu0123/Brain-Tumor-Segmentation)]([https://github.com/raywu0123/Brain-Tumor-Segmentation](https://github.com/raywu0123/Brain-Tumor-Segmentation)), (accessed on 25 September 2021). **Author Contributions:** Conceptualization, S.W., Y.W., H.C., and F.X. ; Methodology, S.W., H.C., and Y.W. ; Software, S.W. and H.C.; Validation, S.W., Y.W., and F.X. ; Formal analysis, Y.W., C.L., and F.X. ; Investigation, S.W., H.C., Y.W., and F.X. ; Resources, F.X. ; Data curation, S.W., H.C., Y.W., and F.X. ; Writing--original draft preparation, Y.W., C.L., and F.X. ; Writing--review and editing, Y.W., F.T.S., H.L., W.T., C.L., F.L., F.H., and F.X. ; Visualization, C.L., Y.W., and F.X. ; Supervision, F.X. ; Project administration, F.X; Funding acquisition, F.X. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Ministry of Science and Technology, Taiwan, ROC, grant numbers 107-2634-F-002-015, 110-2634-F-002-032, 110-2314-B-002-161. **Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by Research Ethics Committee of National Taiwan University Hospital (201708071 RINC, 6 October 2017). **Informed Consent Statement:** Informed consent was waived by the committee. **Data Availability Statement:** The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. **Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. * [PERSON] et al. (2021) [PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; et al. Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks. _Neuro-Oncology_**2021**, _23_, 1560-1568. [CrossRef]
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