Function tissue unit segmentation based on UNext model

Research Article
Open access

Function tissue unit segmentation based on UNext model

Hongyu Sun 1 , Qi Zhang 2* , Qiangdi Zhang 3 , Zifeng Zhou 4
  • 1 HuaZhong University of Science and Technology    
  • 2 Dalhousie University    
  • 3 Beijing University of Technology    
  • 4 University of California, Los Angeles    
  • *corresponding author qi.zhang@dal.ca
TNS Vol.5
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-915371-53-9
ISBN (Online): 978-1-915371-54-6

Abstract

Accurate segmentation for Functional Tissue Units (FTUs) is a challenging issue in past decades. In this study, a model using the dataset of tissue section images will be built to evaluate and mark FTUs across five human organs as clearly as possible. We have the Human Protein Atlas (HPA) as training data and the data from Human BioMolecular Atlas Program (HuBMAP) as testing data. To balance accuracy and inference speed, this study applied Unext, an efficient network based on Unet, as the basic model. We also aim to use some tricks to further improve the performance of the model. First, we used several image enhancement methods to diversify the input image. Second, several structures like Feature Pyramid Network (FPN) and the Atrous Spatial Pyramid Pooling are added to improve model performance and convergence speed. As a result, we successfully segment functional tissue units among images of different sizes. Our proposed model scored 0.56 out of 1.00 by the judge of the competition.

Keywords:

machine learning, image segmentation, Unext.

Sun,H.;Zhang,Q.;Zhang,Q.;Zhou,Z. (2023). Function tissue unit segmentation based on UNext model. Theoretical and Natural Science,5,601-606.
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References

[1]. Hesamian M et al. 2019 Deep learning techniques for medical image segmentation: achievements and challenges Journal of digital imaging 32(4) 582-596

[2]. Ronneberger O et al. 2015 October U-net: Convolutional networks for biomedical image segmentation In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241) Springer Cham

[3]. Amin J et al. 2018 Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87, 290-297.

[4]. Zhang M et al. 2018 RBC semantic segmentation for sickle cell disease based on deformable U-Net. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 695-702) Springer Cham

[5]. Xu G et al. 2021 Levit-unet: Make faster encoders with transformer for medical image segmentation arXiv preprint arXiv:2107.08623

[6]. Lin T Y 2017 Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).

[7]. Chen L C 2017 Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution and fully connected crfs IEEE transactions on pattern analysis and machine intelligence 40(4) 834-848

[8]. Song T 2019 U-next: A novel convolution neural network with an aggregation u-net architecture for gallstone segmentation in ct images IEEE Access 7 166823-166832

[9]. Yun D 2022 ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation IEEE Access vol.10 pp.35932-35941

[10]. Pontén F et al. 2008 The Human Protein Atlas—a tool for pathology The Journal of Pathology: A Journal of the Pathological Society of Great Britain and Ireland 216(4) 387-393

[11]. Lin S et al. 2019 The human body at cellular resolution: the NIH Human Biomolecular Atlas Program.

Cite this article

Sun,H.;Zhang,Q.;Zhang,Q.;Zhou,Z. (2023). Function tissue unit segmentation based on UNext model. Theoretical and Natural Science,5,601-606.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)

ISBN：978-1-915371-53-9(Print) / 978-1-915371-54-6(Online)
Editor：Marwan Omar, Roman Bauer
Conference website: https://www.confciap.org/
Conference date: 25 March 2023
Series: Theoretical and Natural Science
Volume number: Vol.5
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. Hesamian M et al. 2019 Deep learning techniques for medical image segmentation: achievements and challenges Journal of digital imaging 32(4) 582-596

[2]. Ronneberger O et al. 2015 October U-net: Convolutional networks for biomedical image segmentation In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241) Springer Cham

[3]. Amin J et al. 2018 Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87, 290-297.

[4]. Zhang M et al. 2018 RBC semantic segmentation for sickle cell disease based on deformable U-Net. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 695-702) Springer Cham

[5]. Xu G et al. 2021 Levit-unet: Make faster encoders with transformer for medical image segmentation arXiv preprint arXiv:2107.08623

[6]. Lin T Y 2017 Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).

[7]. Chen L C 2017 Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution and fully connected crfs IEEE transactions on pattern analysis and machine intelligence 40(4) 834-848

[8]. Song T 2019 U-next: A novel convolution neural network with an aggregation u-net architecture for gallstone segmentation in ct images IEEE Access 7 166823-166832

[9]. Yun D 2022 ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation IEEE Access vol.10 pp.35932-35941

[10]. Pontén F et al. 2008 The Human Protein Atlas—a tool for pathology The Journal of Pathology: A Journal of the Pathological Society of Great Britain and Ireland 216(4) 387-393

[11]. Lin S et al. 2019 The human body at cellular resolution: the NIH Human Biomolecular Atlas Program.

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