Measurement of endometrial thickness using deep neural network with multi-task learning

Jianchong He; Xiaowen Liang; Yao Lu; Jun Wei; Zhiyi Chen

doi:10.1117/12.2623119

16 February 2022 Measurement of endometrial thickness using deep neural network with multi-task learning

Jianchong He, Xiaowen Liang, Yao Lu, Jun Wei, Zhiyi Chen

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 1208325 (2022) https://doi.org/10.1117/12.2623119
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Endometrial receptivity assessment based on the ultrasound image is a common and non-invasive way in clinician practice. Clinicians consider that the thickness of the endometrium is one of the most important assessment markers, which can be calculated with the endometrial region in ultrasound images. Suffering from low contrast of the boundaries in ultrasound images, it’s a challenge that makes accurate segmentation of endometrial for thickness calculation. An automated assessment framework with a multi-task learning segmentation network is proposed in this paper. The VGGbased U-net is trained with an auxiliary pattern classification task, the losses of different tasks are combined by weighted sum based on uncertainty in the training phase. Experiment shows that the network has a more accurate prediction than single-task learning and the framework does a better thickness calculation.

Citation Download Citation

Jianchong He, Xiaowen Liang, Yao Lu, Jun Wei, and Zhiyi Chen "Measurement of endometrial thickness using deep neural network with multi-task learning", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 1208325 (16 February 2022); https://doi.org/10.1117/12.2623119

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KEYWORDS

Image segmentation

Ultrasonography

Computer programming

Classification systems

Convolution

Image classification

Image processing

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