Mr. Vishwesh Ramanathan Profile

Vishwesh Ramanathan

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Proceedings Article | 7 April 2023 Presentation + Paper

Identification of molecular cell type of breast cancer on digital histopathology images using deep learning and multiplexed fluorescence imaging

Wenchao Han, Alison Cheung, Vishwesh Ramanathan, Dan Wang, Kela Liu, Martin Yaffe, Anne Martel

Proceedings Volume 12471, 1247106 (2023) https://doi.org/10.1117/12.2654943

KEYWORDS: Breast cancer, Image segmentation, Cancer, Deep learning, Tissues, Multiplexing, Education and training, Image classification, Image registration, Proteins

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ER, PR (estrogen, progesterone receptor), and HER2 (human epidermal growth factor receptor 2) status are assessed using immunohistochemistry and reported in standard clinical workflows as they provide valuable information to help treatment planning. The protein Ki67 has also been suggested as a prognostic biomarker but is not routinely evaluated clinically due to insufficient quality assurance. The routine pathological practice usually relies on small biopsies, such that the reduction in consumption is necessary to save materials for special assays. For this purpose, we developed and validated an automatic system for segmenting and identifying the (ER, PR, HER2, Ki67) positive cells from hæmatoxylin and eosin (H&E) stained tissue sections using multiplexed immunofluorescence (MxIF) images at cellular level as a reference standard. In this study, we used 100 tissue-microarray cores sampled from 56 cases of invasive breast cancer. For ER, we extracted cell nucleus images (HoverNet) from the H&E images and assigned each cell nucleus as ER positive vs. negative based on the corresponding MxIF signals (whole cell segmentation with DeepCSeg) upon H&E to MxIF image registration. We trained a Res-Net 18 and validated the model on a separate test-set for classifying the cells as positive vs. negative for ER, and performed the same experiment for the other three markers. We obtained area-under-the- receiver-operating-characteristic-curves (AUCs) of 0.82 (ER), 0.85 (PR), 0.75 (HER2), 0.82 (Ki67) respectively. Our study demonstrates the feasibility of using machine learning to identify molecular status at cellular level directly from the H&E slides.

Proceedings Article | 7 April 2023 Presentation + Paper

Ink removal in whole slide images using hallucinated data

Vishwesh Ramanathan, Wenchao Han, Dina Bassiouny, Eileen Rakovitch, Anne Martel

Proceedings Volume 12471, 124710Y (2023) https://doi.org/10.1117/12.2653281

KEYWORDS: Tissues, Histopathology, Color, Pathology, Deep learning, Quality control, Image classification

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Pathologists regularly use ink markings on histopathology slides to highlight specific areas of interest or orientation, making it an integral part of the workflow. Unfortunately, digitization of these ink-annotated slides hinders any computer-aided analyses, particularly deep learning algorithms, which require clean data free from artifacts. We propose a methodology that can identify and remove the ink markings for the purpose of computational analyses. We propose a two-stage network with a binary classifier for ink filtering and Pix2Pix for ink removal. We trained our network by artificially generating pseudo ink markings using only clean slides, requiring no manual annotation or curation of data. Furthermore, we demonstrate our algorithm’s efficacy over an independent dataset of H&E stained breast carcinoma slides scanned before and after the removal of pen markings. Our quantitative analysis shows promising results, achieving 98.7% accuracy for the binary classifier. For Pix2Pix, we observed a 65.6% increase in structure similarity index, a 21.3% increase in peak signal-to-noise ratio, and a 30% increase in visual information fidelity. As only clean slides are required for training, the pipeline can be adapted to multiple colors of ink markings or new domains, making it easy to deploy over different sets of histopathology slides. Code and trained models are available at: https://github.com/Vishwesh4/Ink-WSI.

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