Abstract
An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan.
Highlights
The advantage of ResNet is that the performance of this model does not reduce the ability to extract features and train the network, even though it is becoming more profound than other architectures [54]
The ResNet model achieves an advanced performance in image classification relative to other models [55]
The decrease in the level of foreground segmentation (FS) in the scar tissue was an indication of irreversible tissue damage, as the current findings showed a 38% decrease in the FS from the scar tissue relative to normal tissue
Summary
Scar tissue develops as a result of tissue damage caused by injury, surgery, or burns. Identifying and evaluating scar tissue is the most crucial step in determining the extent of tissue damage and planning a suitable treatment pathway for scar management and removal [5]. Determining and analyzing the scar regions in the skin tissue remains challenging. The limited field of view often restricts analysis of the entire scar lesion in the whole slide image (WSI). Recent research studies have manually located scar lesions to analyze a region of interest (ROI) in the scar area [6,7]. An in-depth analysis of a scar region is difficult, owing to the comparable color distributions in hematoxylin and eosin (HE)-stained images
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