CARE 2025: Comprehensive Analysis & computing of REal-world medical images
Creators: name: Wang, Bomin
name: Gao, Yibo
name: Wu, Fuping
name: Gao, Shangqi
name: Zhuang, Xiahai
name: Ding, Wangbin
name: Wang, Sihan
name: Odille, Freddy
name: Chen, Bailiang
name: Ma, Yingliang
name: Wu, Lianming
name: Yang, Shan
name: Chen, Yinyin
name: Liu, Yuanye
name: Shi, Nannan
name: Li, Yuxin
name: Shi, Yuxin
name: Wangbin, Ding
name: Sheng, Xicheng
name: Li, Lei
name: Yang, Guang
name: Lin, Xingtao
name: Huang, Liqin
Publisher: name: Zenodo
Year: 2025
Subjects: Medical Image Segmentation
Foundation Models
Real-World medical image analysis
Myocardium pathology segmentation
Liver fibrosis staging and segmentation
Whole heart segmentation
Left atrial and scar quantification and segmentation
MICCAI 2025 challenge

description: Real-world medical imaging data pose distinct challenges, including variability in imaging modalities, distribution shifts due to diverse data acquisition protocols across centers, and irregular regions of interest (ROIs) such as lesions and scars. The advent of foundation models like the Segment Anything Model (SAM) has revolutionized healthcare AI by demonstrating remarkable performance across a range of tasks. However, their effectiveness in real-world medical imaging scenarios remains insufficiently explored, especially for data characterized by high variability and clinical complexity. This limitation is particularly evident in segmentation tasks involving myocardial pathology and lesions or scars, which are often small, irregularly shaped, and structurally intricate. Addressing this gap is essential for adapting foundation models to clinical settings, thereby advancing medical image analysis and improving patient outcomes. In this challenge, we design a track focused on adapting foundation models tofour real-world medical imaging analysis tasks. We also establish independent tracks dedicated to each task. This structure encourages participants to not only explore the potential of foundation models but also to investigate alternative methods tailored to address the complexities of real-world medical imaging analysis. In our first edition CARE 2024, we had 150 teams participating, accepted 23 papers (9 oral presentations), and conducted over 450 online evaluations. Based on CARE 2024, we extend the challenge with the following enhancements: (1) Inclusion of over 850 newly collected cases, comprising 560 liver MRIs, 200 CT scans for whole-heart segmentation, and 100 cardiac MRIs for myocardial pathology segmentation; (2) The challenge introduces an expanded evaluation methodology to support a wider variety of prompts in medical image segmentation. Participants can now utilize point prompts, bounding box prompts, text prompts, and mask prompts, offering greater flexibility and enabling a more versatile and inclusive assessment framework.

[Challenges of real-world medical images]



Misalignment and Motion Artifacts: Real-world imaging of organs like the heart and liver is complicated by misalignments resulting from respiratory motion and cardiac pulsation. These artifacts add complexity to joint analysis and segmentation tasks.

Inhomogeneity of Imaging Data: Medical images collected from various centers differ in modality, resolution, and acquisition parameters. These variations result in significant distribution shifts, which challenge the generalization capabilities of foundation models.

Irregular and Small ROIs: Real-world medical tasks often involve ROIs with irregular shapes and small sizes, such as lesions or scars. These irregularities challenge the ability of foundation models to produce accurate and clinically meaningful segmentation results.


These challenges underscore the need for effective and efficient fine-tuning strategies that adapt foundation models to the unique characteristics of real-world medical imaging data.

[Objectives]



Develop Transfer Learning Approaches: Encourage participants to develop innovative transfer learning or finetuning techniques for adapting foundation models to diverse and complex real-world medical imaging scenarios. 

Foster Generalizability: Evaluate the generality of foundation models across multiple tasks and imagingmodalities, enabling broader clinical applicability.

Benchmark Performance: Provide a comprehensive benchmarking framework for assessing the performance ofalgorithms on five tracks, fostering reproducibility in medical image analysis.


[Datasets]The challenge will release four diverse datasets designed to address specific clinical requirements. These datasets target organs with significant deformation (e.g., heart and liver) and comprise data from over 2100 patients collected from 18 centers across three continents. Key features of the datasets include:



Multi-Continental Representation: Data acquired from centers in three continents ensures diversity in patientdemographics and imaging protocols.

Multi-Modality: A variety of imaging modalities are encompassed, reflecting the heterogeneity of real-worldclinical practice.

Intrinsic Misalignment: Challenges posed by respiratory motion and cardiac pulsation are embedded in thedatasets, simulating real-world conditions.

Missing Data: Scenarios with missing modalities are included to reflect practical limitations in clinical imaging.


[Challenge Tracks]The challenge will feature five tracks, each addressing a specific aspect of medical image analysis:



Transferring Foundation Models: Developing foundation models capable of generalizing across multiple tasks. This track emphasizes the adaptability and generalizability of foundation models on real-world medical image analysis.

Myocardial Pathology Segmentation: Addressing myocardial pathology segmentation, focusing on regions with high variability and irregularity.

Liver Fibrosis Quantification and Analysis: Tackling liver segmentation and fibrosis staging, considering deformations and modality diversity.

Whole Heart Segmentation: Focusing on comprehensive cardiac segmentation, incorporating motion artifacts and anatomical complexity.

Left Atrial and Scar Quantification & Segmentation: The target of this track is to automatically segment LA cavity and quantify LA scars from LGE MRI with a focus on small and irregular ROIs.


The first track will serve as a comprehensive evaluation of the generality of foundation models across the other four tracks. Algorithms will be assessed using diverse metrics to ensure a holistic evaluation and fair ranking.