MSRCN: Multi-task Learning Network for Cell Segmentation and Regression Counting
Pages 486 - 490
Abstract
Accurate cell segmentation and counting play an important role in medical diagnosis. However, the size and shape of cells are varied largely, and the presence of overlapping cells complicates cell counting. Recent studies have shown that multi-task learning methods perform well in deep learning. In specific, we design Multi-task Segmentation Regression Counting Network (MSRCN). For cell segmentation, a multi-scale attention mechanism module is designed to suppress irrelevant regions and learns salient features for a specific task. For cell counting, a regression model is utilized to learn a mapping from cell feature information to target counts. The proposed MSRCN model is analyzed and compared with other states of the art cell segmentation methods and cell counting methods. MSRCN outperforms these methods in all evaluation metrics. The Dice similarity coefficient, root mean square error, and mean absolute error of the proposed method is 0.9316, 2.1215, and 1.5927, respectively. The experiments results show that the proposed method not only improves the functioning of cell segmentation, but also outperforms direct regression counting methods in terms of cell counting.
References
[1]
Mohammed Osman, Rehan M Faridi, and Wendy Sligl. Impaired natural killer cell counts and cytolytic activity in patients with severe covid-19. Blood advances, 4(20):5035–5039, 2020.
[2]
Igor Novitzky-Basso, Carol Chen, and Shiyi Chen. Pretransplant bone marrow cellularity and blood count recovery are not associated with relapse or survival risk following allogeneic stem cell transplant for aml in cr. European Journal of Haematology, 107(3):354–363, 2021.
[3]
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, May 2015.
[4]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015.
[5]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778,2016.
[6]
Zitao Zeng, Weihao Xie, Yunzhe Zhang, and Yao Lu. Ric-unet: An improved neural network based on unet for nuclei segmentation in histology images. Ieee Access, 7:21420–21428, 2019.
[7]
Neeraj Kumar, Ruchika Verma, Sanuj Sharma, Surabhi Bhargava, Abhishek Vahadane, and Amit Sethi. A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE transactions on medical imaging, 36(7):1550– 1560, 2017.
[8]
Peter Naylor, Marick La, Fabien Reyal, and Thomas Walter. Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE transactions on medical imaging, 38(2):448–459,2018.
[9]
Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, and Nasir Rajpoot. Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Medical Image Analysis, 58:101563, 2019.
[10]
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: A nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support, pages 3– 11. Springer,2018.
[11]
Sebastian Ruder. An Overview of Multi-Task Learning in Deep Neural Networks. arXiv e-prints, page arXiv:1706.05098, June 2017.
[12]
Eric Z Chen, Xu Dong, Xiaoxiao Li, Hongda Jiang, Ruichen Rong, and Junyan Wu. Lesion attributes segmentation for melanoma detection with multi-task u-net. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pages 485–488. IEEE, 2019.
[13]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30,2017.
[14]
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146–3154, 2019.
[15]
Yimian Dai, Fabian Gieseke, Stefan Oehmcke, Yiquan Wu, and Kobus Barnard. Attentional feature fusion. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3560– 3569, 2021.
[16]
M Usaj, D Torkar, M Kanduser, and D Miklavcic. Cell counting tool parameters optimization approach for electroporation efficiency determination of attached cells in phase contrast images. Journal of microscopy, 241(3):303–314, 2011.
[17]
Roberto Morelli, Luca Clissa, Roberto Amici, Matteo Cerri, Timna Hitrec, Marco Luppi, Lorenzo Rinaldi, Fabio Squarcio, and Antonio Zoccoli. Automating cell counting in fluorescent microscopy through deep learning with c-resunet. Scientific Reports, 11(1):1– 11, 2021.
[18]
De Rong Loh, Wen Xin Yong, Jullian Yapeter, Karupppasamy Subburaj, and Rajesh Chandramohanadas. A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using mask r-cnn. Computerized Medical Imaging and Graphics, 88:101845, 2021.
[19]
Yao Xue, Nilanjan Ray, Judith Hugh, and Gilbert Bigras. Cell counting by regression using convolutional neural network. In European Conference on Computer Vision, pages 274–290. Springer, 2016.
[20]
Falko Lavitt, Demi J Rijlaarsdam, Dennet van der Linden, Ewelina Weglarz-Tomczak, and Jakub M Tomczak. Deep learning and transfer learning for automatic cell counting in microscope images of human cancer cell lines. Applied Sciences, 11(11):4912, 2021.
[21]
Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A Anastasio, and Hua Li. Deeply-supervised density regression for automatic cell counting in microscopy images. Medical Image Analysis, 68:101892, 2021.
[22]
Joseph Paul Cohen, Genevieve Boucher, Craig A Glastonbury, Henry Z Lo, and Yoshua Bengio. Count-ception: Counting by fully convolutional redundant counting. In Proceedings of the IEEE International conference on computer vision workshops, pages 18-26, 2017.
[23]
Vebjorn Ljosa, Katherine L Sokolnicki, and Anne E Carpenter. Annotated high-throughput microscopy image sets for validation. Nature methods, 9(7):637–637, 2012.
[24]
Kaiming He, Georgia Gkioxari, Piotr Doll´ar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969, 2017.
[25]
Qian Liu, Anna Junker, Kazuhiro Murakami, and Pingzhao Hu. A novel convolutional regression network for cell counting. In 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology (ICBCB), pages 44–49. IEEE, 2019.
[26]
Carlos X Hern´andez, Mohammad M Sultan, and Vijay S Pande. Using deep learning for segmentation and counting within microscopy data. arXiv preprint arXiv:1802.10548, 2018.
[27]
Qian Liu, Anna Junker, Kazuhiro Murakami, and Pingzhao Hu. Automated counting of cancer cells by ensembling deep features. Cells, 8(9):1019, 2019.
Index Terms
- MSRCN: Multi-task Learning Network for Cell Segmentation and Regression Counting
Index terms have been assigned to the content through auto-classification.
Recommendations
Integrating multi-scale blob/curvilinear detector techniques and multilevel sets for automated segmentation of stem cell images
ISBI'09: Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to MacroStudies of differentiation abilities of stem cells have been attracting a lot of attention over the last years. Microscopy can be used to record details of the differentiation process of stem cells under different perturbations and is an important tool ...
CNN-based two-stage cell segmentation improves plant cell tracking
Highlights- A CNN-based two-stage segmentation approach is presented to segment plant cells.
AbstractAutomated tracking of densely packed plant cells in time-lapse live imaging datasets is a challenging task. A local graph matching algorithm was proposed to track such cells by exploiting the tight spatial topology of neighboring ...
Segmentation of Cervical Cell Images
ICPR '10: Proceedings of the 2010 20th International Conference on Pattern RecognitionThe key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the challenges of ...
Comments
Information & Contributors
Information
Published In
October 2022
594 pages
ISBN:9781450398442
DOI:10.1145/3570773
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 December 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ISAIMS 2022
ISAIMS 2022: 2022 3rd International Symposium on Artificial Intelligence for Medicine Sciences
October 13 - 15, 2022
Amsterdam, Netherlands
Acceptance Rates
Overall Acceptance Rate 53 of 112 submissions, 47%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 74Total Downloads
- Downloads (Last 12 months)30
- Downloads (Last 6 weeks)5
Reflects downloads up to 07 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format