Version 1
: Received: 2 November 2024 / Approved: 4 November 2024 / Online: 4 November 2024 (14:33:51 CET)
How to cite:
Jeltsch, P.; Monnin, K.; Jreige, M.; Fernandes-Mendes, L.; Girardet, R.; Dromain, C.; Richiardi, J.; Vietti-Violi, N. MRI Liver Segmentation Protocol Enables More Consistent and Robust Annotations Paving the Way for Advanced Computer-Assisted Analysis. Preprints2024, 2024110198. https://doi.org/10.20944/preprints202411.0198.v1
Jeltsch, P.; Monnin, K.; Jreige, M.; Fernandes-Mendes, L.; Girardet, R.; Dromain, C.; Richiardi, J.; Vietti-Violi, N. MRI Liver Segmentation Protocol Enables More Consistent and Robust Annotations Paving the Way for Advanced Computer-Assisted Analysis. Preprints 2024, 2024110198. https://doi.org/10.20944/preprints202411.0198.v1
Jeltsch, P.; Monnin, K.; Jreige, M.; Fernandes-Mendes, L.; Girardet, R.; Dromain, C.; Richiardi, J.; Vietti-Violi, N. MRI Liver Segmentation Protocol Enables More Consistent and Robust Annotations Paving the Way for Advanced Computer-Assisted Analysis. Preprints2024, 2024110198. https://doi.org/10.20944/preprints202411.0198.v1
APA Style
Jeltsch, P., Monnin, K., Jreige, M., Fernandes-Mendes, L., Girardet, R., Dromain, C., Richiardi, J., & Vietti-Violi, N. (2024). MRI Liver Segmentation Protocol Enables More Consistent and Robust Annotations Paving the Way for Advanced Computer-Assisted Analysis. Preprints. https://doi.org/10.20944/preprints202411.0198.v1
Chicago/Turabian Style
Jeltsch, P., Jonas Richiardi and Naik Vietti-Violi. 2024 "MRI Liver Segmentation Protocol Enables More Consistent and Robust Annotations Paving the Way for Advanced Computer-Assisted Analysis" Preprints. https://doi.org/10.20944/preprints202411.0198.v1
Abstract
Background/Objectives: Recent advancements in artificial intelligence (AI) have spurred interest in developing computer-assisted analysis for imaging examinations. However, the lack of high-quality datasets remains a significant bottleneck. Labeling instructions are critical to improving dataset quality but are often lacking. This study aimed to establish a liver MRI segmentation protocol and assess its impact on annotation quality and inter-reader agreement; Methods: This retrospective study included 20 patients with chronic liver disease. Manual liver segmentations were performed by a radiologist in training and a radiology technician on T2 weighted imaging (wi) and T1wi at the portal venous phase. Based on the inter-reader discrepancies identified after the first segmentation round, a segmentation protocol was established, guiding the second round of segmentation, resulting in a total of 160 segmentations. Dice Similarity Coefficient (DSC) assessed inter-reader agreement pre- and post-protocol with a Wilcoxon signed-rank test. Slice selection at extreme cranial or caudal liver positions was evaluated using the McNemar test; Results: The per-volume DSC significantly increased after protocol implementation for both T2wi (p
Keywords
segmentation; liver; MRI; computer-assisted analysis; radiomics; deep learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
