Name
Title | ||
|---|---|---|
Segmentation of histological images and fibrosis identification with a convolutional neural network. |
Abstract | ||
|---|---|---|
Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, such as fibrosis via Masson's trichrome staining. However, challenges are posed by the 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 11 successive convolutional – rectified linear unit – batch normalization layers. It 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 300,000) trainable parameters than the state-of-the-art, 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 to the problem of concern, and may potentially be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compbiomed.2018.05.015 | Computers in Biology and Medicine |
Keywords | DocType | Volume |
Convolutional neural network,Deep learning,Fibrosis,Histology,Image segmentation | Journal | 98 |
ISSN | Citations | PageRank |
0010-4825 | 2 | 0.36 |
References | Authors | |
32 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohang Fu | 1 | 12 | 2.04 |
| Tong Liu | 2 | 2 | 1.38 |
| Zhaohan Xiong | 3 | 16 | 3.15 |
| Bruce H. Smaill | 4 | 18 | 4.93 |
| Martin K. Stiles | 5 | 7 | 2.12 |
| Jichao Zhao | 6 | 70 | 15.63 |