LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis

Moritz Gross; Sandeep Arora; Steffen Huber; Ahmet S Kücükkaya; John A Onofrey

doi:10.1016/j.dib.2023.109662

LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis

Data Brief. 2023 Oct 10:51:109662. doi: 10.1016/j.dib.2023.109662. eCollection 2023 Dec.

Authors

Moritz Gross^{1

2}, Sandeep Arora¹, Steffen Huber¹, Ahmet S Kücükkaya^{1

2}, John A Onofrey^{1

3

4}

Affiliations

¹ Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States of America.
² Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
³ Department of Urology, Yale University School of Medicine, New Haven, CT, United States of America.
⁴ Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America.

Abstract

Accurate segmentation of liver and tumor regions in medical imaging is crucial for the diagnosis, treatment, and monitoring of hepatocellular carcinoma (HCC) patients. However, manual segmentation is time-consuming and subject to inter- and intra-rater variability. Therefore, automated methods are necessary but require rigorous validation of high-quality segmentations based on a consensus of raters. To address the need for reliable and comprehensive data in this domain, we present LiverHccSeg, a dataset that provides liver and tumor segmentations on multiphasic contrast-enhanced magnetic resonance imaging from two board-approved abdominal radiologists, along with an analysis of inter-rater agreement. LiverHccSeg provides a curated resource for liver and HCC tumor segmentation tasks. The dataset includes a scientific reading and co-registered contrast-enhanced multiphasic magnetic resonance imaging (MRI) scans with corresponding manual segmentations by two board-approved abdominal radiologists and relevant metadata and offers researchers a comprehensive foundation for external validation, and benchmarking of liver and tumor segmentation algorithms. The dataset also provides an analysis of the agreement between the two sets of liver and tumor segmentations. Through the calculation of appropriate segmentation metrics, we provide insights into the consistency and variability in liver and tumor segmentations among the radiologists. A total of 17 cases were included for liver segmentation and 14 cases for HCC tumor segmentation. Liver segmentations demonstrates high segmentation agreement (mean Dice, 0.95 ± 0.01 [standard deviation]) and HCC tumor segmentations showed higher variation (mean Dice, 0.85 ± 0.16 [standard deviation]). The applications of LiverHccSeg can be manifold, ranging from testing machine learning algorithms on public external data to radiomic feature analyses. Leveraging the inter-rater agreement analysis within the dataset, researchers can investigate the impact of variability on segmentation performance and explore methods to enhance the accuracy and robustness of liver and tumor segmentation algorithms in HCC patients. By making this dataset publicly available, LiverHccSeg aims to foster collaborations, facilitate innovative solutions, and ultimately improve patient outcomes in the diagnosis and treatment of HCC.

Keywords: Benchmarking; Hepatocellular carcinoma; Imaging biomarkers; Inter-rater agreement; Inter-rater variability; Liver segmentation; Multiphasic contrast-enhanced magnetic resonance imaging; Tumor segmentation.

Abstract

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