Ultra-TransUNet: ultrasound segmentation framework with spatial-temporal context feature fusion

Bowen Li; Zongwei Zhou; Alan Yuille; Max Allan; Jonathan McLeod

doi:10.1117/12.3006940

1 April 2024 Ultra-TransUNet: ultrasound segmentation framework with spatial-temporal context feature fusion

Bowen Li, Zongwei Zhou, Alan Yuille, Max Allan, Jonathan McLeod

Proceedings Volume 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography; 1293204 (2024) https://doi.org/10.1117/12.3006940
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Image segmentation of ultrasound videos is not an easy task because of the vagueness of ultrasound images, yet it is a helpful application for intra-surgery tumor detection and key organ protection. Previous ultrasound segmentation methods, such as UNet and Swin-UNetR focus on single image segmentation, and natural image video segmentation methods such as VisTR requires a large volume of GPU memory which is not easily trained and applied with limited calculation resources. In this paper, we put forward an ultrasound video segmentation framework called Ultra-TransUNet, which makes use of both temporal and spatial context, to segment ultrasound videos. Based on the Dice metric, our method improved the Dice and sensitivity performance from baseline methods for about 2.6% and 14.1%, respectively, averaged for 3 datasets. In this paper we evaluate our methods on phantom, animal and cadaver labs for segmenting two types of clinically relevant targets: tumors through phantom studies and the ureter in animal and cadaver labs. We hope our algorithm could provide with surgeons real-time support to locate key structures with ultrasound during surgeries, and thus protect patients and improve surgical outcomes.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bowen Li, Zongwei Zhou, Alan Yuille, Max Allan, and Jonathan McLeod "Ultra-TransUNet: ultrasound segmentation framework with spatial-temporal context feature fusion", Proc. SPIE 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography, 1293204 (1 April 2024); https://doi.org/10.1117/12.3006940

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