TeachMe: a web-based teaching system for annotating abdominal lymph nodes

The detection and characterization of lymph nodes through interpreting abdominal medical images are significant for diagnosing and treating colorectal cancer recurrence. However, interpreting abdominal medical images manually is labor-intensive and time-consuming. The related radiology education has...

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Bibliographic Details
Published in:Scientific reports 2022-03, Vol.12 (1), p.5167-5167, Article 5167
Main Authors: Chen, Shuaihua, Huang, Hao, Yang, Xuyang, Wang, Han, Wei, Mingtian, Zhang, Haixian, Wang, Ziqiang, Yi, Zhang
Format: Article
Language:English
Subjects:
692/4020
692/700/1421
Abdomen
Colorectal carcinoma
Data collection
Feedback
Humanities and Social Sciences
Lymph nodes
Lymphatic system
multidisciplinary
Radiology
Science
Science (multidisciplinary)
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Summary:The detection and characterization of lymph nodes through interpreting abdominal medical images are significant for diagnosing and treating colorectal cancer recurrence. However, interpreting abdominal medical images manually is labor-intensive and time-consuming. The related radiology education has many limitations as well. In this context, we seek to build an extensive collection of abdominal medical images with ground truth labels for lymph nodes recognition research and help junior doctors to train their interpretation skills. Therefore, we develop TeachMe, which is a web-based teaching system for annotating abdominal lymph nodes. The system has a three-level annotation-review workflow to construct an expert database of abdominal lymph nodes and a feedback mechanism helping junior doctors to learn the tricks of interpreting abdominal medical images. TeachMe’s functionalities make itself stand out against other platforms. To validate these functionalities, we invite a medical team from Gastrointestinal Surgery Center, West China Hospital, to participate in the data collection workflow and experience the feedback mechanism. With the help of TeachMe, an expert dataset of abdominal lymph nodes has been created and an automated detection model for abdominal lymph nodes with incredible performances has been proposed. Moreover, through three rounds of practicing via TeachMe, our junior doctors’ interpretation skills have been improved.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-08958-8