Convolutional Neural Network Approach for Multiple Sclerosis Lesion Segmentation

被引：0

作者：

Messaoud, Nada Haj ^{[1
,3
]}

Mansour, Asma ^{[1
]}

Ayari, Rim ^{[1
]}

Ben Abdallah, Asma ^{[1
]}

Aissi, Mouna ^{[2
]}

Frih, Mahbouba ^{[2
]}

Bedoui, Mohamed Hedi ^{[1
]}

机构：

[1] Univ Monastir, Lab Technol & Med Imaging, Fac Med, Monastir, Tunisia

[2] Fatouma Bouguiba Hosp, Dept Neurol, Monastir, Tunisia

[3] Univ Monastir, Fac Sci Monastir FSM, Monastir, Tunisia

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2022 | 2022年 / 13756卷

关键词：

Deep Learning; Multiple sclerosis segmentation; MRI; Data augmentation;

D O I：

10.1007/978-3-031-21753-1_52

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays Deep Learning (DL) based automatic segmentation has outperformed traditionalmethods. In the present paper, we are interested in automatic MS lesion segmentation of 2D images based on DL techniques. The main challenge consists in proposing a new model that takes advantage of referenced CNN models: U-Net, ResNet, and DenseNet with a reduced number of parameters and a shorter execution time. To evaluate the proposed approach named "Concat-U-Net", we compared its performance to those of three implemented models, namely U-Net, U-ResNet, and Dense-U-Net. Furthermore, we employed just one modality (FLAIR) from the public ISBI dataset to segment MS lesions accurately. The best Dice value obtained was 0.73, which outperformed those reported in the literature. Our approach reduced the elapsed execution time from 48 s to 7 s. By reducing the number of parameters, an 85.42% time gain was achieved.

引用

页码：540 / 548

页数：9

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