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Region-guided adversarial learning for anatomical landmark detection in uterus ultrasound image
Author(s): Hongjoo Lee; Hak Gu Kim; Hyenok Park; Dongkuk Shin; Yong Man Ro
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Paper Abstract

The length and thickness of the uterus and endometrium are morphology characteristics as important measures for uterine diagnosis. In diagnosing uterine, doctors mark anatomical landmark points of uterus and endometrium in order to measure their length and thickness. However, it is difficult to reliably detect the landmarks of the uterus and endometrium due to the ambiguous boundaries and heterogeneous textures of uterus transvaginal ultrasound image. In this paper, we propose a novel region-guided adversarial learning framework for anatomical landmark detection in transvaginal ultrasound image, aiming at automatically detecting the landmark points of uterus and endometrium of transvaginal ultrasound image to a diagnostical precision. In the proposed adversarial learning scheme, the proposed framework consists of a landmark predictor and two discriminators for the uterus and endometrium. The proposed landmark predictor is to detect the desired landmarks of both uterus and endometrium regions from transvaginal ultrasound image. The discriminator is to determine whether the predicted landmarks of uterus and endometrium are related with their regions or not (i.e., whether the predicted landmark points are on the region boundaries or not.). By adversarial learning between the predictor and the discriminators with uterus and endometrium region images, the performance of the landmark predictor can be improved. In testing, with the trained predictor only, uterus and endometrium landmarks are predicted. Experimental results demonstrated that the proposed method achieved a high accuracy in detecting landmarks of the uterus and endometrium in the ultrasound image.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094937 (15 March 2019); doi: 10.1117/12.2512731
Show Author Affiliations
Hongjoo Lee, KAIST (Korea, Republic of)
Hak Gu Kim, KAIST (Korea, Republic of)
Hyenok Park, KAIST (Korea, Republic of)
Dongkuk Shin, SAMSUNG Medison Co., Ltd. (Korea, Republic of)
Yong Man Ro, KAIST (Korea, Republic of)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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