Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation

被引：31

作者：

Casamitjana, Adria ^{[1
]}

Cata, Marcel ^{[1
]}

Sanchez, Irina ^{[1
]}

Combalia, Marc ^{[1
]}

Vilaplana, Veronica ^{[1
]}

机构：

[1] Univ Politecn Cataluna, Signal Theory & Commun Dept, BarcelonaTech, Barcelona, Spain

来源：

BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2017 | 2018年 / 10670卷

关键词：

D O I：

10.1007/978-3-319-75238-9_33

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this work we approach the brain tumor segmentation problem with a cascade of two CNNs inspired in the V-Net architecture [13], reformulating residual connections and making use of ROI masks to constrain the networks to train only on relevant voxels. This architecture allows dense training on problems with highly skewed class distributions, such as brain tumor segmentation, by focusing training only on the vecinity of the tumor area. We report results on BraTS2017 Training and Validation sets.

引用

页码：381 / 391

页数：11

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