Back to datasets
Dataset assetOpen Source CommunityMedical ImagingLiver Disease
SYSU-FLL-CEUS Dataset
The released SYSU-CEUS dataset contains three types of focal liver lesions (FLL): 186 HCC instances, 109 HEM instances, and 58 FNH instances (i.e., 186 malignant and 167 benign instances). The dataset was collected by the First Affiliated Hospital of Sun Yat-sen University using an Aplio SSA-770A (Toshiba Medical Systems) device. All instances have a resolution of 768 × 576, originate from different patients, and exhibit substantial variation in appearance and enhancement patterns (e.g., size, contrast, shape, and location).
Source
github
Created
Sep 25, 2013
Updated
Feb 21, 2024
Signals
488 views
Availability
Linked source ready
Overview
Dataset description and usage context
SYSU-FLL-CEUS Dataset Overview
Dataset Composition
- Types and Counts: Includes three types of focal liver lesions (FLLs):
- 186 hepatocellular carcinoma (HCC) instances
- 109 hemangioma (HEM) instances
- 58 focal nodular hyperplasia (FNH) instances
- Total: 186 malignant instances and 167 benign instances
Data Source
- Collection Site: First Affiliated Hospital, Sun Yat-sen University
- Equipment: Aplio SSA-770A (Toshiba Medical Systems)
Data Characteristics
- Resolution: 768 × 576
- Diversity: Collected from different patients with a wide range of appearance and enhancement patterns (e.g., size, contrast, shape, location)
Dataset Access
- Full dataset link: Google Drive link
Citation Information
- If using this dataset, please cite the following papers:
- Xiaodan Liang, Qingxing Cao, Rui Huang, Liang Lin, "Recognizing focal liver lesions in contrast-enhanced ultrasound with discriminatively trained spatio-temporal model", Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on, 2014.
- Xiaodan Liang, Liang Lin, Qingxing Cao, Rui Huang, Yongtian Wang, “Recognizing Focal Liver Lesions in CEUS with Dynamically Trained Latent Structured Models”. IEEE TRANSACTIONS ON MEDICAL IMAGING (T-MI), 2015.
Need downstream help?
Pair the dataset with AI analysis and content workflows.
Once the source passes your review, move straight into summarization, transformation, report drafting, or presentation generation with the JuheAI toolchain.