Acta Photonica Sinica, Volume. 53, Issue 1, 0111003(2024)

Boundary Perception Network for Pathological Image Segmentation

Hong HUANG1、*, Yichuan YANG1, Long WANG1, Fujian ZHENG1, and Jian WU2
Author Affiliations
  • 1Key Laboratory of Optoelectronic Technology and System,Ministry of Education,Chongqing University,Chongqing 400044,China
  • 2Head and Neck Cancer Centre,Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital,Chongqing 400030,China
  • show less
    References(29)

    [1] LITJENS G, KOOI T, BEJNORDI B E et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 42, 60-88(2017).

    [2] PRABHUA S, PRASADA K, ROBELS-KELLY A et al. AI-based carcinoma detection and classification using histopathological images: A systematic review[J]. Computers in Biology and Medicine, 144, 105209(2022).

    [3] RAMESH K, KUMAR G K, SWAPNA K et al. A review of medical image segmentation algorithms[J]. EAI Endorsed Transactions on Pervasive Health and Technology, 7, e6(2021).

    [4] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 9, 62-66(2007).

    [5] VINCENT L, SOILLE P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 583-598(1991).

    [6] BOYKOV Y Y, JOLLY M P. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images[C], 105-112(2001).

    [7] HUANG Xiaoming, HE Fuyun, TANG Xiaohu et al. Review on applications of U-Net and its variants in medical image segmentation[J]. Chinese Journal of Biomedical Engineering, 41, 567-576(2022).

    [8] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C], 234-241(2015).

    [9] ZHOU Z, SIDDIQUEE M M R, TAJBAKHSH N et al. UNet++: a nested U-Net architecture for medical image segmentation[C], 3-11(2018).

    [10] IBTEHAZ N, RAHMAN M S. MultiResUNet: Rethinking the u-net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 121, 74-87(2020).

    [11] OKTAY O, SCHLEMPER J, FOLGOC L L et al. Attention U-net: learning where to look for the pancreas[C](2018).

    [12] WANG J S, WANG Y, TAO X et al. PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images[J]. Fundamental Research, 1, 631-640(2021).

    [13] HUANG Hong, WANG Tao, LI Yuan et al. Cancer pathological segmentation network based on depth feature fusion[J]. Acta Photonica Sinica, 51, 0310001(2022).

    [14] ZHAO B C, CHEN X, LI Z et al. Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation[J]. Medical Image Analysis, 65, 101786(2020).

    [15] ZAMORA W, MENDEZ M, CALDERON-RAMIREZ S et al. Enforcing morphological information in fully convolutional networks to improve cell instance segmentation in fluorescence microscopy images[C], 1, 36-46(2021).

    [16] FANG Y, CHEN C, YUAN Y et al. Selective feature aggregation network with area-boundary constraints for polyp segmentation[C], 302-310(2019).

    [17] FAN D P, JI G P, ZHOU T et al. PraNet:parallel reverse attention network for polyp segmentation[C], 263-273(2020).

    [18] CHEN S, DING C, LIU M et al. CPP-Net: context-aware polygon proposal network for nucleus segmentation[J]. IEEE Transactions on Image Processing, 32, 980-994(2023).

    [19] ZHANG R, LI G, LI Z et al. Adaptive context selectionfor polyp segmentation[C], 253-262(2020).

    [20] CHEN J, LU Y, YU Q et al. Transunet: transformers make strong encoders for medical image segmentation[J](2021).

    [21] VALANARASU J, OZA P, HACIHSLILOGLU I et al. Medical transformer: gated axial-attention for medical image segmentation[C], 36-46(2021).

    [22] WANG H, CAO P, WANG J et al. UCTransNet: rethinking the skip connections in u-net from a channel-wise perspective with transformer[C], 36, 2441-2449(2022).

    [23] TAN M X, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks[C], 691-700(2019).

    [24] HU J, SHEN L, SUN G et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

    [25] WANG Q, WU B, ZHU P et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks, 11531-11539(2020).

    [26] YANG Jun, LI Bozan. Semantic segmentation of 3D point cloud based on self-attention feature fusion group convolutional neural network[J]. Optics and Precision Engineering, 30, 840-853(2022).

    [27] SIRINUKUNWATTANA K, PLUIM J P, CHEN H et al. Gland segmentation in colon histology images: The glas challenge contest[J]. Medical Image Analysis, 35, 489-502(2021).

    [28] KUMAR N, VERMA R, SHARMA S et al. A dataset and a technique for generalized nuclear segmentation for computational pathology[J]. IEEE Transactions on Medical Imaging, 36, 1550-1560(2017).

    [29] KUMAR N, VERMA R, ANAND D et al. A multi-organ nucleus segmentation challenge[J]. IEEE Transactions on Medical Imaging, 39, 1380-1391(2019).

    Tools

    Get Citation

    Copy Citation Text

    Hong HUANG, Yichuan YANG, Long WANG, Fujian ZHENG, Jian WU. Boundary Perception Network for Pathological Image Segmentation[J]. Acta Photonica Sinica, 2024, 53(1): 0111003

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Jul. 4, 2023

    Accepted: Aug. 1, 2023

    Published Online: Feb. 1, 2024

    The Author Email: Hong HUANG (hhuang@cqu.edu.cn)

    DOI:10.3788/gzxb20245301.0111003

    Topics