Deep Learning-Based Real-time Ureter Identification in Laparoscopic Colorectal Surgery

医学 结直肠外科 输尿管 普通外科 腹腔镜手术 腹腔镜检查 外科肿瘤学 鉴定(生物学) 外科 腹部外科 植物 生物
作者
Satoshi Narihiro,Daichi Kitaguchi,Hiro Hasegawa,Nobuyoshi Takeshita,Masaaki Ito
出处
期刊:Diseases of The Colon & Rectum [Ovid Technologies (Wolters Kluwer)]
卷期号:67 (10): e1596-e1599
标识
DOI:10.1097/dcr.0000000000003335
摘要

BACKGROUND: Iatrogenic ureteral injury is a serious complication of abdominopelvic surgery. Identifying the ureters intraoperatively is essential to avoid iatrogenic ureteral injury. We developed a model that may minimize this complication. IMPACT OF INNOVATION: We applied a deep learning–based semantic segmentation algorithm to the ureter recognition task and developed a deep learning model called UreterNet. This study aimed to verify whether the ureters could be identified in videos of laparoscopic colorectal surgery. TECHNOLOGY, MATERIALS, AND METHODS: Semantic segmentation of the ureter area was performed using a convolutional neural network–based approach. Feature Pyramid Networks were used as the convolutional neural network architecture for semantic segmentation. Precision, recall, and the Dice coefficient were used as the evaluation metrics in this study. PRELIMINARY RESULTS: We created 14,069 annotated images from 304 videos, with 9537, 2266, and 2266 images in the training, validation, and test data sets, respectively. Concerning ureter recognition performance, the precision, recall, and Dice coefficient for the test data were 0.712, 0.722, and 0.716, respectively. Regarding the real-time performance on recorded videos, it took 71 milliseconds for UreterNet to infer all pixels corresponding to the ureter from a single still image and 143 milliseconds to output and display the inferred results as a segmentation mask on the laparoscopic monitor. CONCLUSIONS: UreterNet is a noninvasive method for identifying the ureter in videos of laparoscopic colorectal surgery and can potentially improve surgical safety. FUTURE DIRECTIONS: Although this deep learning model could lead to the development of an image-navigated surgical system, it is necessary to verify whether UreterNet reduces the occurrence of iatrogenic ureteral injury.
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
羊羊完成签到,获得积分10
刚刚
刚刚
Yuanyuan完成签到,获得积分10
1秒前
sxy0604完成签到,获得积分10
1秒前
我是老大应助syccc采纳,获得10
2秒前
亦瑶发布了新的文献求助10
2秒前
达da应助啊啊啊采纳,获得10
2秒前
一兜兜糖发布了新的文献求助10
3秒前
Ni发布了新的文献求助10
4秒前
Luny完成签到,获得积分20
5秒前
ANNNNN发布了新的文献求助10
6秒前
6秒前
认真飞瑶发布了新的文献求助10
7秒前
8秒前
wwww发布了新的文献求助10
11秒前
11秒前
小小完成签到 ,获得积分10
11秒前
小心完成签到 ,获得积分10
11秒前
斯文败类应助青青草采纳,获得10
12秒前
tong发布了新的文献求助10
13秒前
13秒前
13秒前
13秒前
风中采枫完成签到 ,获得积分10
14秒前
ANNNNN完成签到,获得积分10
15秒前
zhangscience完成签到,获得积分10
15秒前
空曲发布了新的文献求助10
16秒前
16秒前
椰子汁完成签到,获得积分10
18秒前
18秒前
Bigwang发布了新的文献求助10
18秒前
zhangscience发布了新的文献求助10
19秒前
19秒前
毛豆应助ningning采纳,获得10
20秒前
22秒前
23秒前
23秒前
23秒前
gc完成签到 ,获得积分10
23秒前
胖胖龙发布了新的文献求助10
24秒前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
【本贴是提醒信息,请勿应助】请在求助之前详细阅读求助说明!!!! 20000
Evolution 5000
좌파는 어떻게 좌파가 됐나:한국 급진노동운동의 형성과 궤적 2500
Sustainability in Tides Chemistry 1500
La Chine révolutionnaire d'aujourd'hui / Van Min, Kang Hsin 1000
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
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 3044554
求助须知:如何正确求助?哪些是违规求助? 2701739
关于积分的说明 7384800
捐赠科研通 2345718
什么是DOI,文献DOI怎么找? 1241583
科研通“疑难数据库(出版商)”最低求助积分说明 603979
版权声明 595503