Enhancing gland segmentation in colon histology images using an instance-aware diffusion model

被引:6
作者
Sun, Mengxue [1 ]
Wang, Jiale [1 ]
Gong, Qingtao [2 ]
Huang, Wenhui [1 ]
机构
[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China
[2] Ludong Univ, Ulsan Ship & Ocean Coll, Yantai 264025, Peoples R China
基金
中国国家自然科学基金;
关键词
Colon histology images; Gland segmentation; Diffusion model; Instance segmentation; Conditional encoding; NETWORK; NET;
D O I
10.1016/j.compbiomed.2023.107527
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In pathological image analysis, determination of gland morphology in histology images of the colon is essential to determine the grade of colon cancer. However, manual segmentation of glands is extremely challenging and there is a need to develop automatic methods for segmenting gland instances. Recently, due to the powerful noise-to-image denoising pipeline, the diffusion model has become one of the hot spots in computer vision research and has been explored in the field of image segmentation. In this paper, we propose an instance segmentation method based on the diffusion model that can perform automatic gland instance segmentation. Firstly, we model the instance segmentation process for colon histology images as a denoising process based on a diffusion model. Secondly, to recover details lost during denoising, we use Instance Aware Filters and multi-scale Mask Branch to construct global mask instead of predicting only local masks. Thirdly, to improve the distinction between the object and the background, we apply Conditional Encoding to enhance the intermediate features with the original image encoding. To objectively validate the proposed method, we compared several state-of-the-art deep learning models on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset (165 images), the Colorectal Adenocarcinoma Glands (CRAG) dataset (213 images) and the RINGS dataset (1500 images). Our proposed method obtains significantly improved results for CRAG (Object F1 0.853 +/- 0.054, Object Dice 0.906 +/- 0.043), GlaS Test A (Object F1 0.941 +/- 0.039, Object Dice 0.939 +/- 0.060), GlaS Test B (Object F1 0.893 +/- 0.073, Object Dice 0.889 +/- 0.069), and RINGS dataset (Precision 0.893 +/- 0.096, Dice 0.904 +/- 0.091). The experimental results show that our method significantly improves the segmentation accuracy, and the experiment results demonstrate the efficacy of the method.
引用
收藏
页数:10
相关论文
共 58 条
  • [1] Anand D, 2019, INT CONF SYST SIGNAL, P219, DOI [10.1109/IWSSIP.2019.8787328, 10.13140/rg.2.2.34770.61125]
  • [2] Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images
    Awan, Ruqayya
    Sirinukunwattana, Korsuk
    Epstein, David
    Jefferyes, Samuel
    Qidwai, Uvais
    Aftab, Zia
    Mujeeb, Imaad
    Snead, David
    Rajpoot, Nasir
    [J]. SCIENTIFIC REPORTS, 2017, 7 : 2220 - 2243
  • [3] SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
    Badrinarayanan, Vijay
    Kendall, Alex
    Cipolla, Roberto
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (12) : 2481 - 2495
  • [4] SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks
    Che, Tongtong
    Zheng, Yuanjie
    Yang, Yunshuai
    Hou, Sujuan
    Jia, Weikuan
    Yang, Jie
    Gong, Chen
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 30 : 6036 - 6049
  • [5] BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation
    Chen, Hao
    Sun, Kunyang
    Tian, Zhi
    Shen, Chunhua
    Huang, Yongming
    Yan, Youliang
    [J]. 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2020), 2020, : 8570 - 8578
  • [6] DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
    Chen, Hao
    Qi, Xiaojuan
    Yu, Lequan
    Heng, Pheng-Ann
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 2487 - 2496
  • [7] DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
    Chen, Liang-Chieh
    Papandreou, George
    Kokkinos, Iasonas
    Murphy, Kevin
    Yuille, Alan L.
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) : 834 - 848
  • [8] Chen SF, 2023, Arxiv, DOI arXiv:2211.09788
  • [9] Score-based diffusion models for accelerated MRI
    Chung, Hyungjin
    Ye, Jong Chul
    [J]. MEDICAL IMAGE ANALYSIS, 2022, 80
  • [10] Dabass M., 2021, Informatics in Medicine Unlocked, V27, DOI [10.1016/j.imu.2021.100784, DOI 10.1016/J.IMU.2021.100784]