Bhatia D, Aviles Verdera J, Kitzberger M, Tripathy S, Bustos Vivas M, Kratzsch L, Knupfer A, Hutter J (2025)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2025
Publisher: Springer
Series: Lecture Notes in Computer Science
City/Town: Cham
Book Volume: 16149
Pages Range: 137-147
Conference Proceedings Title: Skin Image Analysis, and Computer-Aided Pelvic Imaging for Female Health. 10th International Workshop, ISIC 2025, and First International Workshop, CAPI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings
Event location: Daejeon
ISBN: 9783032058249
DOI: 10.1007/978-3-032-05825-6_13
Inter-observer variability, lack of standardization, the necessity to document incidental findings, and the clinical demand for rapid diagnostic support limit the efficiency and reliability of pelvic MRI, emphasizing the need for automated analysis of uterine MRI scans. This work introduces a real-time, deep learning-based tool designed to automatically generate structured analysis reports directly from sagittal T2-weighted pelvic MR images. Utilizing real-time scanner interfacing and state-of-the-art 3D nnU-Net architecture with a Residual Encoder preset trained on a publicly available dataset, the proposed system accurately segments the uterine wall, uterine cavity, uterine fibroids, and Nabothian cysts. Post-processing of the predicted segmentation enables the generation of comprehensive structured HTML reports that include precise uterine volumetric measurements and detailed lesion assessments for fibroids and Nabothian cysts. The performance of the tool was validated on two independent datasets from different clinical sites, varying in magnetic field strength, and scanner vendor. Its real-time inference capability was also confirmed. The segmentation model showed reliable performance uterine wall, uterine cavity and uterine myoma (mean dice coefficient of 0.82, 0.77 and 0.78 respectively) and a mean dice coefficient of 0.43 for Nabothian cysts. Reports were consistently generated in real-time within an average time of 60 s. By providing immediate, standardized, and reproducible analyses, the developed tool is well-positioned for seamless integration into clinical radiological workflows.
APA:
Bhatia, D., Aviles Verdera, J., Kitzberger, M., Tripathy, S., Bustos Vivas, M., Kratzsch, L.,... Hutter, J. (2025). Real-Time Automated Analysis and Reporting of Uterine MRI. In M. Emre Celebi, Johanna Paula Müller, Catarina Barata, Allan Halpern, Philipp Tschandl, Marc Combalia, Yuan Liu, Kumar Abhishek, Joanna Jaworek-Korjakowska, Moi Hoon Yap, Katharina Breininger, Maximilian Lindholz, Jana Hutter, Richard Ruppel, Smiti Tripathy, Franziska Mathis-Ullrich, Stefanie Burghaus, Matthias May (Eds.), Skin Image Analysis, and Computer-Aided Pelvic Imaging for Female Health. 10th International Workshop, ISIC 2025, and First International Workshop, CAPI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings (pp. 137-147). Daejeon: Cham: Springer.
MLA:
Bhatia, Deepak, et al. "Real-Time Automated Analysis and Reporting of Uterine MRI." Proceedings of the 10th International Workshop on Skin Image Analysis, ISIC 2025 and 1st International Workshop on Computer-Aided Pelvic Imaging for Female Health, CAPI 2025, Held in Conjunction with 28th International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2025, Daejeon Ed. M. Emre Celebi, Johanna Paula Müller, Catarina Barata, Allan Halpern, Philipp Tschandl, Marc Combalia, Yuan Liu, Kumar Abhishek, Joanna Jaworek-Korjakowska, Moi Hoon Yap, Katharina Breininger, Maximilian Lindholz, Jana Hutter, Richard Ruppel, Smiti Tripathy, Franziska Mathis-Ullrich, Stefanie Burghaus, Matthias May, Cham: Springer, 2025. 137-147.
BibTeX: Download