Multi-scale Deep Learning Architecture for Nucleus Detection in Renal Cell Carcinoma Microscopy Image

• URL: http://arxiv.org/abs/2104.13557v1
• Date: Wed, 28 Apr 2021 03:36:02 GMT
• Title: Multi-scale Deep Learning Architecture for Nucleus Detection in Renal Cell Carcinoma Microscopy Image
• Authors: Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, Anand Rajan
• Abstract summary: Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of intratumoral heterogeneity in the study of renal cancer. In this paper, we introduce a deep learning-based detection model for cell classification on IHC stained histology images. Our model maps the multi-scale pyramid features and saliency information from local bounded regions and predicts the bounding box coordinates through regression.
• Score: 7.437224586066945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of intratumoral heterogeneity in the study of renal cancer. ccRCC originates from the epithelial lining of proximal convoluted renal tubules. These cells undergo abnormal mutations in the presence of Ki67 protein and create a lump-like structure through cell proliferation. Manual counting of tumor cells in the tissue-affected sections is one of the strongest prognostic markers for renal cancer. However, this procedure is time-consuming and also prone to subjectivity. These assessments are based on the physical cell appearance and suffer wide intra-observer variations. Therefore, better cell nucleus detection and counting techniques can be an important biomarker for the assessment of tumor cell proliferation in routine pathological investigations. In this paper, we introduce a deep learning-based detection model for cell classification on IHC stained histology images. These images are classified into binary classes to find the presence of Ki67 protein in cancer-affected nucleus regions. Our model maps the multi-scale pyramid features and saliency information from local bounded regions and predicts the bounding box coordinates through regression. Our method validates the impact of Ki67 expression across a cohort of four hundred histology images treated with localized ccRCC and compares our results with the existing state-of-the-art nucleus detection methods. The precision and recall scores of the proposed method are computed and compared on the clinical data sets. The experimental results demonstrate that our model improves the F1 score up to 86.3% and an average area under the Precision-Recall curve as 85.73%.

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