MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network

• Published in: BMC medical informatics and decision making Vol. 22; no. 1; pp. 90 - 11
• Main Authors: Ali, Haider, Haq, Imran Ul, Cui, Lei, Feng, Jun
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04-04-2022; BioMed Central; BMC
• Subjects: Algorithms; Artificial neural networks; Attention; Automation; Cancer; Convolutional neural network; Decoding; Deep learning; Digital imaging; Health informatics; Histology, Pathological; Histopathology; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Information processing; Medical diagnosis; Medical imaging; Medical research; Methods; Mitosis; Morphology; Multiscale attention learning; Neural networks; Neural Networks, Computer; Nuclei; Nuclei (cytology); Nuclei segmentation; Pathology; Physicians; Semantics; Spatial data; Technology application; Tumors; China; Deep learning; Nuclei segmentation; Multiscale attention learning; Convolutional neural network
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-022-01826-5

The digital pathology images obtain the essential information about the patient's disease, and the automated nuclei segmentation results can help doctors make better decisions about diagnosing the disease. With the speedy advancement of convolutional neural networks in image processing, deep learning has been shown to play a significant role in the various analysis of medical images, such as nuclei segmentation, mitosis detection and segmentation etc. Recently, several U-net based methods have been developed to solve the automated nuclei segmentation problems. However, these methods fail to deal with the weak features representation from the initial layers and introduce the noise into the decoder path. In this paper, we propose a multiscale attention learning network (MSAL-Net), where the dense dilated convolutions block captures more comprehensive nuclei context information, and a newly modified decoder part is introduced, which integrates with efficient channel attention and boundary refinement modules to effectively learn spatial information for better prediction and further refine the nuclei cell of boundaries.
Both qualitative and quantitative results are obtained on the publicly available MoNuseg dataset. Extensive experiment results verify that our proposed method significantly outperforms state-of-the-art methods as well as the vanilla Unet method in the segmentation task. Furthermore, we visually demonstrate the effect of our modified decoder part.
The MSAL-Net shows superiority with a novel decoder to segment the touching and blurred background nuclei cells obtained from histopathology images with better performance for accurate decoding.