Overview

Schema Version

https://atlas.rsna.org/schemas/2025-11/dataset.json

Name

Federated Imaging of Liver Tumor Segmentation (FILTS)

Link

https://pubs.rsna.org/doi/full/10.1148/ryai.220082

Indexing

Keywords: federated learning, liver CT, tumor segmentation, LiTS, multi-institutional

Content: GI, OI, CT

RadLex: RID11221, RID49459, RID7467, RID10321

SNOMED: 126851005

Author(s)

Guotai Luo

Tong Liu

Junjun Lu

Comments

Dataset established to train and validate a federated learning model for liver CT using the publicly available LiTS dataset plus cases from two additional independent institutions.

Date

Published: 2023

References

[1] Luo G, Liu T, Lu J, et al.. "Influence of data distribution on federated learning performance in tumor segmentation". Radiology: Artificial Intelligence. 2023. doi:10.1148/ryai.220082.

Dataset

Motivation

To enable federated learning research on liver tumor segmentation and to study the influence of inter-site data distribution differences on model performance.

External data

Built using the publicly available Liver Tumor Segmentation (LiTS) dataset plus cases from two additional independent institutions.