Estimation of incomplete organ-coverage using 3D fully convolutional networks

3 April 2023 Estimation of incomplete organ-coverage using 3D fully convolutional networks

Hrishikesh Deshpande, Axel Saalbach, Tim Harder, Edna Coetser, Shlomo Gotman, Thomas Buelow, Christian Wülker

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Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124641E (2023) https://doi.org/10.1117/12.2653986
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Deviations from the MR acquisition guidelines could lead to images with serious quality concerns such as incompletely imaged anatomies, which might require re-examinations and could result in missed pathologies. In this paper, we propose a deep learning method to automatically estimate the coverage of the target anatomy and to predict the extent of an anatomy outside the present field-of-view (FOV). For this purpose, we employed a 3D fully-convolutional neural network operating at multiple resolution levels. The proposed solution could be employed to propose a correct FOV setting in case of organ-coverage issues while patient is on the table and could be incorporated as a retrospective tool for quality monitoring and staff training. Our method was evaluated for four abdominal organs - liver, spleen, and left and right kidneys - in 40 magnetic resonance (MR) images from the publicly available Combined Healthy Abdominal Organ Segmentation (CHAOS) dataset. We obtained median extent-detection errors of 5.5-7.3mm or 3-4 voxels in the superior or inferior position in a dataset with average anatomical clippings of 24.8-43.6mm for four partially missing organs in the given FOV.

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Citation Download Citation

Hrishikesh Deshpande, Axel Saalbach, Tim Harder, Edna Coetser, Shlomo Gotman, Thomas Buelow, and Christian Wülker "Estimation of incomplete organ-coverage using 3D fully convolutional networks", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641E (3 April 2023); https://doi.org/10.1117/12.2653986

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