Abstract
The National Cancer Institute (NCI) supports numerous research consortia that rely on imaging technologies to study cancerous tissues. To foster collaboration and innovation in this field, the Image Analysis Working Group (IAWG) was created in 2019. As multiplexed imaging techniques grow in scale and complexity, more advanced computational methods are required beyond traditional approaches like segmentation and pixel intensity quantification. In 2022, the IAWG held a virtual hackathon focused on addressing challenges in analyzing complex, high‐dimensional datasets from fixed cancer tissues. The hackathon addressed key challenges in three areas: (1) cell type classification and assessment, (2) spatial data visualization and translation, and (3) scaling image analysis for large, multi‐terabyte datasets. Participants explored the limitations of current automated analysis tools, developed potential solutions, and made significant progress during the hackathon. Here we provide a summary of the efforts and resultant resources and highlight remaining challenges facing the research community as emerging technologies are integrated into diverse imaging modalities and data analysis platforms.
Original language | English |
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Number of pages | 17 |
Journal | Molecular Oncology |
Early online date | 10 Feb 2025 |
DOIs | |
Publication status | Published - 10 Feb 2025 |
Externally published | Yes |
Austrian Fields of Science 2012
- 102003 Image processing
- 301114 Cell biology
- 102019 Machine learning
- 102035 Data science
Keywords
- artifact removal
- artifacts
- cancer
- computational scalability
- domain representation
- image analysis