Segmenting Brain Tumor with an Improved U-Net Architecture

被引：1

作者：

Tan, Der Sheng ^{[1
]}

Tam, Wei Qiang ^{[1
]}

Nisar, Humaira ^{[1
]}

Yeap, Kim Ho ^{[1
]}

机构：

[1] Univ Tunku Abdul Rahman Kampar, Dept Elect Engn, Kampar 31900, Perak, Malaysia

来源：

2022 IEEE-EMBS CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES, IECBES | 2022年

关键词：

Brain Tumor; Deep Learning; MRI; U-Net; SEGMENTATION;

D O I：

10.1109/IECBES54088.2022.10079331

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To aid in the clinical diagnosis of brain tumors, magnetic resonance imaging (MRI) is frequently used. The amount of time it takes to manually segment MRI images depends on the radiologist's level of expertise. In this paper, a novel U-Net architecture for segmenting images of brain tumors is proposed. We have evaluated BraTS 2020 dataset with an improved U-Net structure with a dropout layer inserted between the encoder and decoder to reduce overfitting. By comparing with other U-Net architectures, our method has shown a promising result with dice coefficients 70.40%, 69.08% and 73.03%, for whole tumor (WT), tumor core (TC) and enhanced tumor (ET).

引用

页码：72 / 77

页数：6

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