Abstract
Malignant melanoma is a severe and aggressive type of skin cancer, with a rapid decrease in survival rate if not diagnosed and treated at an early stage. Histopathological examination of hematoxylin and eosin stained tissue biopsies under a light microscope is currently the gold standard for diagnosis. However, this manual examination is a difficult and time-consuming task, and diagnosis is often subject to intra- and inter-observer variability. With more pathology departments starting to convert conventional glass slides into digital resources, a Computer Aided Diagnostic (CAD) system that can automate part of the diagnostic process will help address these challenges. It is expected to reduce examination time, increase diagnostic accuracy, and reduce diagnostic variations. An important initial step in developing such a system is an automated epidermis segmentation algorithm, since several important diagnostic factors are within or seen relatively to the epidermis’ location. In this paper, we propose a new epidermis segmentation technique built on Convolutional Neural Networks. We trained an U-net based architecture end-to-end, with sim 380hbox {k} overlapping high resolution image patches at 512 times 512 pixels, extracted and augmented from 36 digitized histopathological images from two different clinical sites, to discriminate pixels as either epidermal or non-epidermal. The proposed technique was evaluated on 33 test images, where we achieved a mean Positive Predictive Value at 0.89pm 0.16, Sensitivity at 0.92pm 0.1, Dice Similarity Coefficient at 0.89pm 0.13 and a Matthews Correlation Coefficient at 0.89pm 0.11, showing a superior performance when compared to existing techniques. Our algorithm also proves to be robust to variations in staining, tissue thickness and laboratory pre-processing.
Highlights
Malignant melanoma is one of the cancer types in Norway with highest increase in incident rate [1], placing Norway amongst the countries in the world with highest melanoma incidence and mortality, when looking at age-standardized rates [2]
In the following pages we propose a robust automatic segmentation technique, built on Convolutional Neural Network (CNN) and the U-net architecture
This paper presents a new method for segmenting the epidermal areas in hematoxylin and eosin stained whole slide histopathological images, using Convolutional Neural Network and a U-net based architecture
Summary
Malignant melanoma is one of the cancer types in Norway with highest increase in incident rate [1], placing Norway amongst the countries in the world with highest melanoma incidence and mortality, when looking at age-standardized rates [2]. Malignant melanoma is among the most aggressive types of skin cancer and if not treated early, the tumor is likely to thicken and progress to a more invasive stage. It can invade nearby lymphatic -and/or blood vessels and rapidly spread to other parts of the body. This will cause the 5-year survival rate to drop dramatically from 80–90% (male-female); to only 14–24% depending on cancer stage at time of diagnosis [3]. Detection and accurate diagnosis at an early stage is of the utmost importance
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