‘Squeeze & excite’ guided few-shot segmentation of volumetric images

人工智能 分割 计算机视觉 弹丸 计算机科学 图像分割 模式识别(心理学) 化学 有机化学
作者
Abhijit Guha Roy,Shayan Siddiqui,Sebastian Pölsterl,Nassir Navab,Christian Wachinger
出处
期刊:Medical Image Analysis [Elsevier]
卷期号:59: 101587-101587 被引量:117
标识
DOI:10.1016/j.media.2019.101587
摘要

Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this shortcoming by learning a new class from a few annotated support examples. We introduce, a novel few-shot framework, for the segmentation of volumetric medical images with only a few annotated slices. Compared to other related works in computer vision, the major challenges are the absence of pre-trained networks and the volumetric nature of medical scans. We address these challenges by proposing a new architecture for few-shot segmentation that incorporates 'squeeze & excite' blocks. Our two-armed architecture consists of a conditioner arm, which processes the annotated support input and generates a task-specific representation. This representation is passed on to the segmenter arm that uses this information to segment the new query image. To facilitate efficient interaction between the conditioner and the segmenter arm, we propose to use 'channel squeeze & spatial excitation' blocks – a light-weight computational module – that enables heavy interaction between both the arms with negligible increase in model complexity. This contribution allows us to perform image segmentation without relying on a pre-trained model, which generally is unavailable for medical scans. Furthermore, we propose an efficient strategy for volumetric segmentation by optimally pairing a few slices of the support volume to all the slices of the query volume. We perform experiments for organ segmentation on whole-body contrast-enhanced CT scans from the Visceral Dataset. Our proposed model outperforms multiple baselines and existing approaches with respect to the segmentation accuracy by a significant margin. The source code is available at https://github.com/abhi4ssj/few-shot-segmentation.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
刚刚
我要发文章关注了科研通微信公众号
刚刚
刚刚
喔喔完成签到,获得积分10
2秒前
2秒前
大模型应助Frank Xu采纳,获得10
2秒前
樊樊发布了新的文献求助10
4秒前
NexusExplorer应助SXM采纳,获得10
4秒前
1112发布了新的文献求助10
4秒前
Li发布了新的文献求助30
4秒前
时尚傲之完成签到,获得积分10
4秒前
转山转水转出了自我完成签到,获得积分10
5秒前
5秒前
5秒前
pcr163应助hhhhh采纳,获得50
5秒前
5秒前
6秒前
Jerry发布了新的文献求助10
7秒前
7秒前
7秒前
阿航发布了新的文献求助10
8秒前
9秒前
我还能学完成签到,获得积分10
9秒前
22222发布了新的文献求助10
9秒前
小蘑菇应助仲达采纳,获得10
10秒前
10秒前
小蘑菇应助maxueni采纳,获得10
11秒前
淳于汲完成签到,获得积分10
11秒前
ZHDG完成签到,获得积分10
11秒前
11秒前
香蕉觅云应助寻度采纳,获得10
11秒前
美好的惜天完成签到 ,获得积分10
12秒前
科目三应助小瓦片采纳,获得10
12秒前
12秒前
Li完成签到,获得积分10
13秒前
14秒前
ziyu完成签到,获得积分10
15秒前
16秒前
Jerry完成签到,获得积分20
16秒前
18秒前
高分求助中
The ACS Guide to Scholarly Communication 2500
Sustainability in Tides Chemistry 2000
Pharmacogenomics: Applications to Patient Care, Third Edition 1000
Studien zur Ideengeschichte der Gesetzgebung 1000
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Threaded Harmony: A Sustainable Approach to Fashion 810
Genera Insectorum: Mantodea, Fam. Mantidæ, Subfam. Hymenopodinæ (Classic Reprint) 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3083043
求助须知:如何正确求助?哪些是违规求助? 2736283
关于积分的说明 7540658
捐赠科研通 2385697
什么是DOI,文献DOI怎么找? 1265066
科研通“疑难数据库(出版商)”最低求助积分说明 612909
版权声明 597702