NnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images. Issue 2 (30th June 2022)

• Record Type: Journal Article
• Authors: Pettit, Rowland W.; Marlatt, Britton B.; Corr, Stuart J.; Havelka, Jim; Rana, Abbas
• Is Part Of: Annals of surgery open. Volume 3:Issue 2(2022)
• Page Start: e155
• Publication Date: 2022-06-30
• Subjects: computed tomography -- deep learning -- liver -- machine learning -- nnunet -- segmentation -- solid organ transplantation -- transplantation -- volume; Surgery -- Periodicals; Surgery -- History -- Periodicals; General Surgery; Surgery; History; Periodicals; 616
• DOI: 10.1097/AS9.0000000000000155
• Languages: English
• ISSNs: 2691-3593

Abstract

Abstract : Background: Recipient donor matching in liver transplantation can require precise estimations of liver volume. Currently utilized demographic-based organ volume estimates are imprecise and nonspecific. Manual image organ annotation from medical imaging is effective; however, this process is cumbersome, often taking an undesirable length of time to complete. Additionally, manual organ segmentation and volume measurement incurs additional direct costs to payers for either a clinician or trained technician to complete. Deep learning-based image automatic segmentation tools are well positioned to address this clinical need. Objectives: To build a deep learning model that could accurately estimate liver volumes and create 3D organ renderings from computed tomography (CT) medical images. Methods: We trained a nnU-Net deep learning model to identify liver borders in images of the abdominal cavity. We used 151 publicly available CT scans. For each CT scan, a board-certified radiologist annotated the liver margins (ground truth annotations). We split our image dataset into training, validation, and test sets. We trained our nnU-Net model on these data to identify liver borders in 3D voxels and integrated these to reconstruct a total organ volume estimate. Results: The nnU-Net model accurately identified the border of the liver with a mean overlap accuracy of 97.5% compared with ground truth annotations. Our calculated volume estimates achieved a mean percent error of 1.92% + 1.54% on the test set. Conclusions: Precise volume estimation of livers from CT scans is accurate using a nnU-Net deep learning architecture. Appropriately deployed, a nnU-Net algorithm is accurate and quick, making it suitable for incorporation into the pretransplant clinical decision-making workflow.