Preserving dense features for Ki67 nuclei detection

Seyed Hossein Mirjahanmardi; Melanie Dawe; Anthony Fyles; Wei Shi; Fei-Fei Liu; Susan Done; April Khademi

doi:10.1117/12.2611212

4 April 2022 Preserving dense features for Ki67 nuclei detection

Seyed Hossein Mirjahanmardi, Melanie Dawe, Anthony Fyles, Wei Shi, Fei-Fei Liu, Susan Done, April Khademi

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120390Y (2022) https://doi.org/10.1117/12.2611212
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Nuclei detection is a key task in Ki67 proliferation index estimation in breast cancer images. Deep learning algorithms have shown strong potential in nuclei detection tasks. However, they face challenges when applied to pathology images with dense medium and overlapping nuclei since _ne details are often diluted or completely lost by early maxpooling layers. This paper introduces an optimized UV-Net architecture, specifically developed to recover nuclear details with high-resolution through feature preservation for Ki67 proliferation index computation. UV-Net achieves an average F1-score of 0.83 on held-out test patch data, while other architectures obtain 0.74- 0.79. On tissue microarrays (unseen) test data obtained from multiple centers, UV-Net's accuracy exceeds other architectures by a wide margin, including 9-42% on Ontario Veterinary College, 7-35% on Protein Atlas and 0.3-3% on University Health Network.

Conference Presentation

Citation Download Citation

Seyed Hossein Mirjahanmardi, Melanie Dawe, Anthony Fyles, Wei Shi, Fei-Fei Liu, Susan Done, and April Khademi "Preserving dense features for Ki67 nuclei detection", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120390Y (4 April 2022); https://doi.org/10.1117/12.2611212

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