Fast, Low-resource, Accurate, Robust, and Effectual Medical Image Analysis (FLARE) 2025
Creators: name: Ma, Jun
name: Gu, Song
name: Wang, Bo
Publisher: name: Zenodo
Year: 2025
Subjects: Efficiency
Pan-cancer Segmentation
Foundation Models
MICCAI 2025 challenge

description: Medical imaging plays a pivotal role in modern healthcare, enabling accurate diagnosis, prognosis, and treatment planning for a wide range of diseases. Despite significant advances in deep learning-based methods for medical image analysis, several challenges remain in creating general models that are fast, resource-efficient, accurate,and robust across diverse clinical and computational tasks. Addressing these challenges is vital to improving healthcare globally, especially in resource-constrained settings. During the past four years, we have organized four challenges to address these limitations with community efforts.



FLARE 2021: four abdominal organs segmentation in CT scans; Data: 511 CT scans

FLARE 2022: 13 abdominal organs segmentation in CT scans; Data: 2300 CT scans

FLARE 2023: 13 abdominal organs and pan-cancer segmentation in CT scans; Data: 4500 CT scans

FLARE 2024: pan-cancer segmentation, abdominal CT organ segmentation on laptops, and unsupervised domain adaptation for abdominal organ segmentation in MRI Scans; Data: 10,000+ CT scans, 4000+ MRI scans.


The top algorithms can simultaneously segment 13 organs and various abdominal lesions within 10 seconds for a 3D CT scan with over 1,000,000 voxels, which substantially improved the segmentation accuracy, efficiency, and generalization ability. In FLARE 2025, we aim to address critical clinical needs and technical challenges in disease diagnosis and the development of foundational and multimodal AI systems that can generalize across medical imaging modalities. Specifically, the challenge is designed to advance the state of the art in medical image analysis through six innovative subtasks:



Pan-cancer segmentation: develop robust algorithms capable of segmenting lesions across multiple disease types in CT scans

Abdominal CT Organ Segmentation on Laptop: develop resource-efficient methods that can perform accurate segmentation on computationally limited devices, promoting accessibility in low-resource settings.

Unsupervised Domain Adaptation for Abdominal Organ Segmentation in MRI and PET Scans: develop adaptation methods from CT to unseen modalities (i.e., MRI and PET) without extensive labeled data, fostering generalizability and scalability.

Foundation Model for 3D CT and MRI: develop versatile, pre-trained models that can serve as a basis for a variety of downstream diagnosis tasks in 3D medical imaging.

Multimodal Model for Medical Image Parsing: develop generalist vision-language models that can handle classification, detection, counting, measuring, and regression tasks for various image modalities.

Agentic System for Medical Image Analysis: develop large language model-based systems capable of interacting with users to call medical image analysis tools to solve various medical image analysis tasks.


The envisioned impact of this challenge includes driving innovation in resource-efficient, generalizable, and multimodal AI systems, setting new benchmarks for performance and applicability in medical image analysis.

From a biomedical perspective, successful methodologies will directly impact patient care by enabling accurate and reliable imaging solutions accessible to diverse healthcare settings, bridging gaps in diagnostic capacity between resource-rich and resource-limited environments.