Automated proliferation index calculation for skin melanoma biopsy images using machine learning. (April 2021) - Alheejawi, Salah; Berendt, Richard; Jha, Naresh; Maity, Santi P.; Mandal, Mrinal
Highlights: Fully automated technique for nuclei segmentation & PI Calculation. Uses novel deep learning network to achieve superior performance. About 95 % Lymph Node segmentation accuracy is achieved. For PI calculation, an average error less than 5 % is achieved. An important step for fully automated melanoma grading. Abstract: The Proliferation Index (PI) is an important diagnostic, predictive and prognostic parameter used for evaluating different types of cancer. This paper presents an automated technique to measure the PI values for skin melanoma images using machine learning algorithms. The proposed technique first analyzes a Mart-1 stained histology image and generates a region of interest (ROI) mask for the tumor. The ROI mask is then used to locate the tumor regions in the corresponding Ki-67 stained image. The nuclei in the Ki-67 ROI are then segmented and classified using a Convolutional Neural Network (CNN), and the PI value is calculated based on the number of the active and the passive nuclei. Experimental results show that the proposed technique can robustly segment (with 94 % accuracy) and classify the nuclei with a low computational complexity and the calculated PI values have less than 4 % average error.