Towards real world medical image analysis
Creators: 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, Jiyao
name: Shi, Nannan
name: Gao, Zheyao
name: Li, Yuxin
name: Shi, Yuxin
name: Sheng, Xicheng
name: Chen, Yiming
name: Li, Lei
name: Yang, Guang
name: Lin, Xingtao
name: Huang, Liqin
name: Zhuang, Xiahai
Publisher: name: Zenodo
Year: 2024
Subjects: Foundation Model
Generalizability
Real-World medical image analysis
Myocardium pathology segmentation
Liver fibrosis staging and segmentation
Whole heart segmentation
Left atrial and scar quantification and segmentation
MICCAI 2024 challenges

description: Many foundation models for medical image analysis, such as Segment Anything Model (SAM), have been released and proved to be useful in multiple tasks. However, their effectiveness on real world medical imaging data has not been explored. For example, specific images targeted on organs with large deformation, i.e., heart and liver, exhibit greater challenges for analysis.

[challenges]First, the misalignment caused by the respiratory motion and cardiac pulsation increases the complexity of performing joint analysis on these data. Second, the inhomogeneity of real-world medical images poses a challenge, including the diversity of modality and the distribution shift caused by collection from various centers.Third, it could be more challenging for those foundation model to work on irregular ROIs, such as lesions or scars, whose size can be very small and shape irregular. Hence, developing effective and efficient transfer learning approaches to fully utilize those foundation models for real world medical image segmentation is of great values.

[our contribution]In this challenge, we set up a fair and public stage for developing and validating algorithms and applications of transferring foundation models to diverse real-world medical images to address specific practical medical image analysis problems, as well as using conventional methods without pre-trained models.

[Datasets]Four specific datasets will be released grouped by clinical requirements, targeted on organs with grand large  deformation, i.e., heart and liver, and consisting of over 1250 patients from three continents. The diversity of datasets manifests in the following aspects, i.e., multi-continents: collected from over 18 centers across three continents, multi-modality: various modalities are encompassed, misalignment: inherent misalignment exists caused by the respiratory motion and cardiac pulsation, and missing data: refers to the modality missing occurred in practice.

[Track]Five tracks will be held in this challenge, including one comprehensive issue and four specific tracks with corresponding images and clinical problems, namely, (1) Transferring Foundation model track, (2) MyoPS ++, (3) LiFS, (4) Whole Heart Segmentation ++, (5) LAScarQS++. Specifically, the first track aims at a uniform TransferringFoundation model for the generality across the other four tracks, namely to address all or partial tasks. The uniform model will undergo comprehensive evaluation, with various metrics being integrated for ranking purposes.