分割
人工智能
计算机科学
接头(建筑物)
计算机视觉
模式识别(心理学)
图像分割
任务(项目管理)
尺度空间分割
工程类
结构工程
系统工程
作者
Natalia Valderrama,Ioannis Pitsiorlas,Luisa Vargas,Pablo Arbeláez,María A. Zuluaga
标识
DOI:10.1109/isbi53787.2023.10230406
摘要
We propose the first joint-task learning framework for brain and vessel segmentation (JoB-VS) from Time-of-Flight Magnetic Resonance images. Unlike state-of-the-art vessel segmentation methods, our approach avoids the pre-processing step of implementing a model to extract the brain from the volumetric input data. Skipping this additional step makes our method an end-to-end vessel segmentation framework. JoB-VS uses a lattice architecture that favors the segmentation of structures of different scales (e.g., the brain and vessels). Its segmentation head allows the simultaneous prediction of the brain and vessel mask. Moreover, we generate data augmentation with adversarial examples, which our results demonstrate to enhance the performance. JoB-VS achieves 70.03% mean AP and 69.09% F1-score in the OASIS-3 dataset and is capable of generalizing the segmentation in the IXI dataset. These results show the adequacy of JoB-VS for the challenging task of vessel segmentation in complete TOF-MRA images.
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