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Abstract #1395

Robust 3D Landmark Detection Framework for One-Stop Automated Pelvic MRI Prescription

Tatsuki Koike1, Akira Kudo1, Takuya Fuchigami1, Atsushi Tachibana1, Ayaka Ikegawa1, Wataru Yokohama1, Kenta Sakuragi1, Yoshiro Kitamura1, Masatoshi Hori2, and Noriyuki Tomiyama2
1Fujifilm Corporation, Tokyo, Japan, 2Osaka University Graduate School of Medicine, Osaka, Japan

Synopsis

Keywords: Other AI/ML, AI/ML Software, Landmark Detection, Auto Prescription, Deep Learning

Motivation: Automating the prescription of uterine body and cervix in the short-axis to assess endometrial cancer invasion has been challenging due to anatomical variations from diseases and the necessity of a two-step prescription process involving pre-scan/main-scan.

Goal(s): Our goal was to streamline this process into a one-stop automated workflow.

Approach: We developed a novel deep learning-based positioning method that detects 3D landmarks from 3D scout pre-scan and T2-weighted sagittal main-scan images.

Results: Our method achieved technician acceptance rates of 85.3% for uterine body and 97.1% for cervix prescriptions on a dataset primarily consisting of cases with lesions, demonstrating robustness against various diseases and artifacts.

Impact: The automated one-stop workflow enables single-button operation for pelvic MRI, including the challenging short-axis positioning of the uterine body and cervix. It reduces prescription variability among technicians and ensures reproducible imaging, even in anatomically complex cases due to diseases.

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Keywords