Deep Learning‐Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging

主旨 人工智能 医学 分割 接收机工作特性 试验装置 精确性和召回率 模式识别(心理学) 曲线下面积 深度学习 放射科 计算机科学 内科学 间质细胞 药代动力学
作者
Minling Zhuo,Xing Chen,Jingjing Guo,Qingfu Qian,Ensheng Xue,Zhikui Chen
出处
期刊:Journal of Ultrasound in Medicine [Wiley]
卷期号:43 (9): 1661-1672 被引量:1
标识
DOI:10.1002/jum.16489
摘要

Purpose To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). Methods A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning‐based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). Results Among the compared models, SegNeXt‐ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. Conclusion This two‐stage SegNeXt‐ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance.
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
玩命的十三完成签到 ,获得积分10
1秒前
寂寞的诗云完成签到,获得积分10
3秒前
我爱科研完成签到 ,获得积分10
3秒前
4秒前
Bin_Liu发布了新的文献求助10
5秒前
She完成签到,获得积分10
5秒前
8秒前
Raki完成签到,获得积分10
9秒前
22完成签到 ,获得积分10
9秒前
Echo_1995完成签到,获得积分10
12秒前
徐慕源完成签到,获得积分10
12秒前
able发布了新的文献求助10
13秒前
呜呜完成签到 ,获得积分10
14秒前
14秒前
CQ完成签到 ,获得积分10
15秒前
漂亮天真完成签到,获得积分10
16秒前
gmc完成签到 ,获得积分10
16秒前
怡然白竹完成签到 ,获得积分10
18秒前
懵懂的海露完成签到,获得积分10
22秒前
testz完成签到,获得积分10
24秒前
25秒前
一一一完成签到,获得积分10
28秒前
翊然甜周完成签到,获得积分10
28秒前
28秒前
zdnn完成签到,获得积分10
30秒前
TLDX发布了新的文献求助10
33秒前
鳄鱼蛋完成签到,获得积分10
34秒前
luwenxuan完成签到,获得积分10
34秒前
34秒前
奋斗跳跳糖完成签到,获得积分10
34秒前
小白加油完成签到 ,获得积分10
35秒前
35秒前
星辰大海应助大橙子采纳,获得10
35秒前
36秒前
繁荣的新晴完成签到,获得积分20
37秒前
闫星宇完成签到,获得积分10
37秒前
辻诺完成签到 ,获得积分10
37秒前
AR完成签到,获得积分10
37秒前
37秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038184
求助须知:如何正确求助?哪些是违规求助? 3575908
关于积分的说明 11373872
捐赠科研通 3305715
什么是DOI,文献DOI怎么找? 1819255
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022