A Tumour and Liver Automatic Segmentation (ATLAS) Dataset on Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma

Félix Quinton (), Romain Popoff, Benoît Presles, Sarah Leclerc, Fabrice Meriaudeau, Guillaume Nodari, Olivier Lopez, Julie Pellegrinelli, Olivier Chevallier, Dominique Ginhac, Jean-Marc Vrigneaud and Jean-Louis Alberini
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Félix Quinton: Institut de Chimie Moléculaire de l’Université de Bourgogne, ICMUB UMR CNRS 6302, Université Bourgogne Franche-Comté, 21000 Dijon, France
Romain Popoff: Institut de Chimie Moléculaire de l’Université de Bourgogne, ICMUB UMR CNRS 6302, Université Bourgogne Franche-Comté, 21000 Dijon, France
Benoît Presles: Institut de Chimie Moléculaire de l’Université de Bourgogne, ICMUB UMR CNRS 6302, Université Bourgogne Franche-Comté, 21000 Dijon, France
Sarah Leclerc: Institut de Chimie Moléculaire de l’Université de Bourgogne, ICMUB UMR CNRS 6302, Université Bourgogne Franche-Comté, 21000 Dijon, France
Fabrice Meriaudeau: Institut de Chimie Moléculaire de l’Université de Bourgogne, ICMUB UMR CNRS 6302, Université Bourgogne Franche-Comté, 21000 Dijon, France
Guillaume Nodari: Service de Médecine Nucléaire, Centre Georges-François Leclerc, 21000 Dijon, France
Olivier Lopez: Service de Médecine Nucléaire, Centre Georges-François Leclerc, 21000 Dijon, France
Julie Pellegrinelli: Service de Médecine Nucléaire, Centre Georges-François Leclerc, 21000 Dijon, France
Olivier Chevallier: Service de Radiologie et Imagerie Medicale Diagnostique et Therapeutique, Centre Hospitalier Universitaire, 21000 Dijon, France
Dominique Ginhac: Laboratoire Imagerie et Vision Artificielle (ImViA), Universite de Bourgogne Franche-Comté, 21000 Dijon, France
Jean-Marc Vrigneaud: Institut de Chimie Moléculaire de l’Université de Bourgogne, ICMUB UMR CNRS 6302, Université Bourgogne Franche-Comté, 21000 Dijon, France
Jean-Louis Alberini: Institut de Chimie Moléculaire de l’Université de Bourgogne, ICMUB UMR CNRS 6302, Université Bourgogne Franche-Comté, 21000 Dijon, France

Data, 2023, vol. 8, issue 5, 1-9

Abstract: Liver cancer is the sixth most common cancer in the world and the fourth leading cause of cancer mortality. In unresectable liver cancers, especially hepatocellular carcinoma (HCC), transarterial radioembolisation (TARE) can be considered for treatment. TARE treatment involves a contrast-enhanced magnetic resonance imaging (CE-MRI) exam performed beforehand to delineate the liver and tumour(s) in order to perform dosimetry calculation. Due to the significant amount of time and expertise required to perform the delineation process, there is a strong need for automation. Unfortunately, the lack of publicly available CE-MRI datasets with liver tumour annotations has hindered the development of fully automatic solutions for liver and tumour segmentation. The “Tumour and Liver Automatic Segmentation” (ATLAS) dataset that we present consists of 90 liver-focused CE-MRI covering the entire liver of 90 patients with unresectable HCC, along with 90 liver and liver tumour segmentation masks. To the best of our knowledge, the ATLAS dataset is the first public dataset providing CE-MRI of HCC with annotations. The public availability of this dataset should greatly facilitate the development of automated tools designed to optimise the delineation process, which is essential for treatment planning in liver cancer patients.

Keywords: MRI; liver; liver tumours; TARE; hepatocellular carcinoma (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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