MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks

被引：1

作者：

Yan, Benjamin B. ^{[1
]}

Wei, Yujia ^{[1
]}

Jagtap, Jaidip Manikrao M. ^{[1
]}

Moassefi, Mana ^{[1
]}

Garcia, Diana V. Vera ^{[1
]}

Singh, Yashbir ^{[1
]}

Vahdati, Sanaz ^{[1
]}

Faghani, Shahriar ^{[1
]}

Erickson, Bradley J. ^{[1
]}

Conte, Gian Marco ^{[1
]}

机构：

[1] Mayo Clin, Rochester, MN 55901 USA

来源：

BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2021, PT II | 2022年 / 12963卷

关键词：

MRI; Glioblastoma; Segmentation; NEUROONCOLOGY;

D O I：

10.1007/978-3-031-09002-8_7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study, we focus on Task 1 of the 2021 Multimodal Brain Tumor Segmentation (BraTS) challenge. We present a modified U-net model aimed at improving the segmentation of glioblastomas, reducing the computation timewithout compromising detection sensitivity. Our automated approach takes multimodal MR images as input, generates a bounding box of the brain volume, and combines the model predictions at the 2D slice level into a full 3D segmentation that is written into a NIfTI file. On the official 2021 BraTS test set of 570 cases, the model obtained median Dice scores of 0.80, 0.87, and 0.87, as well as median 95% Hausdorff distances of 2.45, 4.64, and 6.40 for the enhancing tumor, tumor core, and whole tumor regions, respectively.

引用

页码：80 / 89

页数：10

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