Auto-segmentation of thoraco-abdominal organs in free breathing pediatric dynamic MRI

Yusuf Akhtar; Jayaram K. Udupa; Yubing Tong; Tiange Liu; Caiyun Wu; Dewey Odhner; Joseph M. McDonough; Carina Lott; Abbie Clark; Jason B. Anari; Patrick Cahill; Drew A. Torigian

doi:10.1117/12.2654995

3 April 2023 Auto-segmentation of thoraco-abdominal organs in free breathing pediatric dynamic MRI

Yusuf Akhtar, Jayaram K. Udupa, Yubing Tong, Tiange Liu, Caiyun Wu, Dewey Odhner, Joseph M. McDonough, Carina Lott, Abbie Clark, Jason B. Anari, Patrick Cahill, Drew A. Torigian

Author Affiliations +

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 124660T (2023) https://doi.org/10.1117/12.2654995
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Quantitative analysis of the dynamic properties of thoraco-abdominal organs such as lungs during respiration could lead to more accurate surgical planning for disorders such as Thoracic Insufficiency Syndrome (TIS). This analysis can be done from semi-automatic delineations of the aforesaid organs in scans of the thoraco-abdominal body region. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for this application, although automatic segmentation of the organs in these images is very challenging. In this paper, we describe an auto-segmentation system we built and evaluated based on dMRI acquisitions from 95 healthy subjects. For the three recognition approaches, the system achieves a best average location error (LE) of about one voxel for the lungs. The standard deviation (SD) of LE is about one to two voxels. For the delineation approach, the average Dice Coefficient (DC) is about 0.95 for the lungs. The standard deviation of DC is about 0.01 to 0.02 for the lungs. The system seems to be able to cope with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data and on other thoraco-abdominal organs including liver, kidneys, and spleen.

Conference Presentation

Citation Download Citation

Yusuf Akhtar, Jayaram K. Udupa, Yubing Tong, Tiange Liu, Caiyun Wu, Dewey Odhner, Joseph M. McDonough, Carina Lott, Abbie Clark, Jason B. Anari, Patrick Cahill, and Drew A. Torigian "Auto-segmentation of thoraco-abdominal organs in free breathing pediatric dynamic MRI", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124660T (3 April 2023); https://doi.org/10.1117/12.2654995

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