DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

一般化 人工智能 分割 模式识别(心理学) 深度学习 多发性硬化 计算机科学 机器学习 数学 医学 数学分析 精神科
作者
Reda Abdellah Kamraoui,Vinh‐Thong Ta,Thomas Tourdias,Boris Mansencal,José V. Manjón,Pierrick Coupé
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:76: 102312-102312 被引量:50
标识
DOI:10.1016/j.media.2021.102312
摘要

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However, state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy aims to produce a robust prediction despite the risk of generalization failure of some individual networks. Second, we propose a hierarchical specialization learning (HSL) by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. Finally, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
TMX完成签到,获得积分20
1秒前
soldatJiang发布了新的文献求助10
2秒前
NL14D发布了新的文献求助10
2秒前
wq1020完成签到,获得积分10
2秒前
2秒前
科研通AI5应助孟欣玥采纳,获得20
3秒前
3秒前
Ava应助什么东西这么好看采纳,获得10
3秒前
超级丝发布了新的文献求助10
3秒前
TMX发布了新的文献求助10
6秒前
星星完成签到,获得积分10
6秒前
wenbinvan完成签到,获得积分0
6秒前
8秒前
科研通AI2S应助SAVP采纳,获得10
8秒前
Lycerdoctor发布了新的文献求助10
8秒前
李健应助wudan采纳,获得10
9秒前
9秒前
ANmin发布了新的文献求助10
9秒前
Inory007发布了新的文献求助10
10秒前
10秒前
桐桐应助冰棍采纳,获得10
10秒前
牧歌完成签到,获得积分0
10秒前
烟花应助怡然的一斩采纳,获得10
10秒前
11秒前
11秒前
SciGPT应助Nxxxxxx采纳,获得10
11秒前
12秒前
丘比特应助汪汪采纳,获得10
12秒前
千余发布了新的文献求助10
12秒前
田様应助爱听歌的树叶采纳,获得10
14秒前
zwy109发布了新的文献求助10
14秒前
loski发布了新的文献求助10
15秒前
鸭梨发布了新的文献求助10
16秒前
忧郁含海发布了新的文献求助10
17秒前
顾矜应助圆滚滚采纳,获得10
17秒前
所所应助平常的紫蓝采纳,获得10
19秒前
20秒前
21秒前
子弹头完成签到,获得积分10
21秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988838
求助须知:如何正确求助?哪些是违规求助? 3531250
关于积分的说明 11252914
捐赠科研通 3269838
什么是DOI,文献DOI怎么找? 1804820
邀请新用户注册赠送积分活动 881943
科研通“疑难数据库(出版商)”最低求助积分说明 809028