3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge

被引：2

作者：

Choi, Yoonseok ^{[1
]}

Al-Masni, Mohammed A. ^{[1
]}

Kim, Dong-Hyun ^{[1
]}

机构：

[1] Yonsei Univ, Dept Elect & Elect Engn, Coll Engn, Seoul, South Korea

来源：

BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2021, PT I | 2022年 / 12962卷

关键词：

Brain tumor segmentation; Pyramid pooling module; U-Net; Glioblastoma; 3D semantic segmentation; Multimodal MRI;

D O I：

10.1007/978-3-031-08999-2_28

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a 3D version of the Contextual Multi-scale Multi-level Network (3D CMM-Net) with deeper encoder depth for automated semantic segmentation of different brain tumors in the BraTS2021 challenge. The proposed network has the capability to extract and learn deeper features for the task of multi-class segmentation directly from 3D MRI data. The overall performance of the proposed network gave Dice scores of 0.7557, 0.8060, and 0.8351 for enhancing tumor, tumor core, and whole tumor, respectively on the local-test dataset.

引用

页码：333 / 343

页数：11

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