Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs

You Hao; Jayaram K. Udupa; Yubing Tong; Tiange Liu; Caiyun Wu; Dewey Odhner; Drew A. Torigian

doi:10.1117/12.3006617

29 March 2024 Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs

You Hao, Jayaram K. Udupa, Yubing Tong, Tiange Liu, Caiyun Wu, Dewey Odhner, Drew A. Torigian

Author Affiliations +

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 1292814 (2024) https://doi.org/10.1117/12.3006617
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Organ segmentation is a fundamental requirement in medical image analysis. Many methods have been proposed over the past 6 decades for segmentation. A unique feature of medical images is the anatomical information hidden within the image itself. To bring natural intelligence (NI) in the form of anatomical information accumulated over centuries into deep learning (DL) AI methods effectively, we have recently introduced the idea of hybrid intelligence (HI) that combines NI and AI and a system based on HI to perform medical image segmentation. This HI system has shown remarkable robustness to image artifacts, pathology, deformations, etc. in segmenting organs in the Thorax body region in a multicenter clinical study. The HI system utilizes an anatomy modeling strategy to encode NI and to identify a rough container region in the shape of each object via a non-DL-based approach so that DL training and execution are applied only to the fuzzy container region. In this paper, we introduce several advances related to modeling of the NI component so that it becomes substantially more efficient computationally, and at the same time, is well integrated with the DL portion (AI component) of the system. We demonstrate a 9-40 fold computational improvement in the auto-segmentation task for radiation therapy (RT) planning via clinical studies obtained from 4 different RT centers, while retaining state-of-the-art accuracy of the previous system in segmenting 11 objects in the Thorax body region.

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Citation Download Citation

You Hao, Jayaram K. Udupa, Yubing Tong, Tiange Liu, Caiyun Wu, Dewey Odhner, and Drew A. Torigian "Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 1292814 (29 March 2024); https://doi.org/10.1117/12.3006617

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