Hepatic Vessel Map (HVM): An Expert-Annotated CT Dataset for Clinically Applicable AI in Liver Vascular Segmentation and Surgical Planning.
Authors
Affiliations (15)
Affiliations (15)
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, 510080, China.
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
- Medical imaging center, Peking University Shenzhen Hospital, Shenzhen, 518036, China.
- Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
- Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China.
- Departments of Radiology, Guangxi Medical University Cancer Hospital, Nanning, 530012, China.
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, China.
- Department of Radiology, The University of Hong Kong - Shenzhen Hospital, Shenzhen, China.
- Medical imaging center, Peking University Shenzhen Hospital, Shenzhen, 518036, China. [email protected].
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China. [email protected].
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China. [email protected].
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China. [email protected].
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China. [email protected].
- Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China. [email protected].
Abstract
Precise delineation of hepatic and portal venous anatomy is crucial for the diagnosis of liver disease, surgical planning, and prognosis prediction. Current three-dimensional visualization of these complex vascular structures relies on manual or semi-automated CT segmentation, which is time-consuming and operator-dependent. Although artificial intelligence (AI) presents a promising alternative, existing methods remain constrained by the scarcity of publicly available datasets with fine-grained vascular annotations and inadequate validation in real-world diseased liver populations, which represent the majority of patients undergoing hepatic procedures. To address this gap, we present the Hepatic Vessel Map (HVM) Dataset, a dual-center resource comprising contrast-enhanced CT scans from 282 patients with over 4,1400 slices and 4,8300 annotations, each with meticulously annotated hepatic veins, portal veins (to third-order branches), and liver tumors. The dataset comprises a substantial proportion of cases with underlying hepatic pathology and has been validated for use in preoperative planning for major hepatectomy, ensuring both clinical relevance and model generalizability. This dataset supports: 1) development and benchmarking of robust hepatic and portal venous segmentation models; 2) vasoimcs research through quantitative analysis of vascular morphology, topology, and radiomic features; 3) generation of patient-specific 3D "digital vascular roadmaps" to enhance surgical precision and safety. As such, this dataset establishes a foundational resource for advancing AI-driven innovations in hepatobiliary surgery and intervention.