Abstract
AbstractBrain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 150 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence
Reference160 articles.
1. Akil M, Saouli R, Kachouri R et al (2020) Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Med Image Anal 63:101692
2. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imag 30(4):449–459
3. Andermatt S, Pezold S, Cattin P (2016) Multi-dimensional gated recurrent units for the segmentation of biomedical 3d-data. In: Deep learning and data labeling for medical applications, pp. 142–151. Springer
4. Andermatt S, Pezold S, Cattin P (2017) Multi-dimensional gated recurrent units for brain tumor segmentation. In: International MICCAI BraTS Challenge. Pre-Conference Proceedings, pp. 15–19
5. Bae W, Lee S, Lee Y, Park B, Chung M, Jung KH (2019) Resource optimized neural architecture search for 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 228–236. Springer
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