A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Abstrak
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care.
Penulis (33)
Lidia Garrucho
Kaisar Kushibar
Claire-Anne Reidel
Smriti Joshi
Richard Osuala
Apostolia Tsirikoglou
Maciej Bobowicz
Javier del Riego
Alessandro Catanese
Katarzyna Gwoździewicz
Maria-Laura Cosaka
Pasant M. Abo-Elhoda
Sara W. Tantawy
Shorouq S. Sakrana
Norhan O. Shawky-Abdelfatah
Amr Muhammad Abdo-Salem
Androniki Kozana
Eugen Divjak
Gordana Ivanac
Katerina Nikiforaki
Michail E. Klontzas
Rosa García-Dosdá
Meltem Gulsun-Akpinar
Oğuz Lafcı
Ritse Mann
Carlos Martín-Isla
Fred Prior
Kostas Marias
Martijn P. A. Starmans
Fredrik Strand
Oliver Díaz
Laura Igual
Karim Lekadir
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓