Segmentation of histological images and fibrosis identification with a convolutional neural network

TL;DR

This paper introduces a novel, efficient convolutional neural network for accurate segmentation of histological images, particularly for fibrosis detection, outperforming existing models with fewer parameters and high robustness.

Contribution

The authors developed the first deep CNN specifically designed for histological image segmentation, achieving high accuracy with significantly fewer parameters and improved robustness.

Findings

Achieved a Dice similarity coefficient of 0.947 on cardiac histological images.

Outperformed state-of-the-art CNNs in segmentation accuracy.

Used 100 times fewer trainable parameters, reducing overfitting.

Abstract

Segmentation of histological images is one of the most crucial tasks for many biomedical analyses including quantification of certain tissue type. However, challenges are posed by high variability and complexity of structural features in such images, in addition to imaging artifacts. Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations. An accurate and robust automated segmentation method is of high interest. We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain. The network comprises of 11 successive convolutional - rectified linear unit - batch normalization layers, and outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947. With 100 times fewer (only 300 thousand) trainable parameters, our CNN is less susceptible to overfitting, and is efficient. Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly. To the best of our knowledge, this is the first deep CNN tailored for the problem of concern, and may be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment.