Adapting SAM to histopathology images for tumor bud segmentation in colorectal cancer

Ziyu Su; Wei Chen; Sony Annem; Usama Sajjad; Mostafa Rezapour; Wendy L. Frankel; Metin N. Gurcan; M. Khalid Khan Niazi

doi:10.1117/12.3006517

3 April 2024 Adapting SAM to histopathology images for tumor bud segmentation in colorectal cancer

Ziyu Su, Wei Chen, Sony Annem, Usama Sajjad, Mostafa Rezapour, Wendy L. Frankel, Metin N. Gurcan, M. Khalid Khan Niazi

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129330C (2024) https://doi.org/10.1117/12.3006517
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Colorectal cancer (CRC) is the third most common cancer in the United States. Tumor Budding (TB) detection and quantification are crucial yet labor-intensive steps in determining the CRC stage through the analysis of histopathology images. To help with this process, we adapt the Segment Anything Model (SAM) on the CRC histopathology images to segment TBs using SAM-Adapter. In this approach, we automatically take task-specific prompts from CRC images and train the SAM model in a parameter-efficient way. We compare the predictions of our model with the predictions from a trained-from-scratch model using the annotations from a pathologist. As a result, our model achieves an intersection over union (IoU) of 0.65 and an instance-level Dice score of 0.75, which are promising in matching the pathologist’s TB annotation. We believe our study offers a novel solution to identify TBs on H&E-stained histopathology images. Our study also demonstrates the value of adapting the foundation model for pathology image segmentation tasks.

Citation Download Citation

Ziyu Su, Wei Chen, Sony Annem, Usama Sajjad, Mostafa Rezapour, Wendy L. Frankel, Metin N. Gurcan, and M. Khalid Khan Niazi "Adapting SAM to histopathology images for tumor bud segmentation in colorectal cancer", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129330C (3 April 2024); https://doi.org/10.1117/12.3006517

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