Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network - PubMed (original) (raw)
Comparative Study
Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network
Yuanpu Xie et al. Med Image Comput Comput Assist Interv. 2015 Oct.
Abstract
Robust cell detection serves as a critical prerequisite for many biomedical image analysis applications. In this paper, we present a novel convolutional neural network (CNN) based structured regression model, which is shown to be able to handle touching cells, inhomogeneous background noises, and large variations in sizes and shapes. The proposed method only requires a few training images with weak annotations (just one click near the center of the object). Given an input image patch, instead of providing a single class label like many traditional methods, our algorithm will generate the structured outputs (referred to as proximity patches). These proximity patches, which exhibit higher values for pixels near cell centers, will then be gathered from all testing image patches and fused to obtain the final proximity map, where the maximum positions indicate the cell centroids. The algorithm is tested using three data sets representing different image stains and modalities. The comparative experiments demonstrate the superior performance of this novel method over existing state-of-the-art.
Figures
Fig. 1
The CNN architecture used in the proposed structured regression model. C, M and F represents the convolutional layer, max pooling layer, and fully connected layer, respectively. The purple arrows from the last layer illustrate the mapping between the final layer's outputs to the final proximity patch.
Fig. 2
(A): The training data generation process. Each original image has a proximity mask of the same size and each local image patch has an proximity patch used as the structured label. (B) The fusion process. Each pixel receives predictions from it's neighborhoods. For example, the red dot collects all the predictions from its 25 neighboring pixels and an average value will be assigned as final result. In this figure, we only display 4 out of 25 proximity patches.
Fig. 3
Cell detection results on three sample images from the three data sets. Yellow dots represent the detected cell centers. The ground truth annotations are represented by green circles for better illustrations.
Fig. 4
Precision-recall curves of the four variations of the proposed algorithm on three data sets. SR-5 achieves almost the same results as SR-1. The proposed SR-1 significantly outperforms the other two pixel-wise methods using CNN.
Similar articles
- Efficient and robust cell detection: A structured regression approach.
Xie Y, Xing F, Shi X, Kong X, Su H, Yang L. Xie Y, et al. Med Image Anal. 2018 Feb;44:245-254. doi: 10.1016/j.media.2017.07.003. Epub 2017 Jul 26. Med Image Anal. 2018. PMID: 28797548 Free PMC article. - Cell Segmentation Using a Similarity Interface With a Multi-Task Convolutional Neural Network.
Ramesh N, Tasdizen T. Ramesh N, et al. IEEE J Biomed Health Inform. 2019 Jul;23(4):1457-1468. doi: 10.1109/JBHI.2018.2885544. Epub 2018 Dec 7. IEEE J Biomed Health Inform. 2019. PMID: 30530343 - Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection.
Xue Y, Bigras G, Hugh J, Ray N. Xue Y, et al. IEEE Trans Med Imaging. 2019 Nov;38(11):2632-2641. doi: 10.1109/TMI.2019.2907093. Epub 2019 Mar 25. IEEE Trans Med Imaging. 2019. PMID: 30908206 - Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images.
Xie Y, Kong X, Xing F, Liu F, Su H, Yang L. Xie Y, et al. Med Image Comput Comput Assist Interv. 2015 Oct;9351:374-382. doi: 10.1007/978-3-319-24574-4_45. Epub 2015 Nov 18. Med Image Comput Comput Assist Interv. 2015. PMID: 28083567 Free PMC article. - White blood cells detection and classification based on regional convolutional neural networks.
Kutlu H, Avci E, Özyurt F. Kutlu H, et al. Med Hypotheses. 2020 Feb;135:109472. doi: 10.1016/j.mehy.2019.109472. Epub 2019 Nov 4. Med Hypotheses. 2020. PMID: 31760248
Cited by
- The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model.
Iparraguirre-Villanueva O, Alvarez-Risco A, Herrera Salazar JL, Beltozar-Clemente S, Zapata-Paulini J, Yáñez JA, Cabanillas-Carbonell M. Iparraguirre-Villanueva O, et al. Vaccines (Basel). 2023 Jan 31;11(2):312. doi: 10.3390/vaccines11020312. Vaccines (Basel). 2023. PMID: 36851190 Free PMC article. - Novel transfer learning schemes based on Siamese networks and synthetic data.
Kenneweg P, Stallmann D, Hammer B. Kenneweg P, et al. Neural Comput Appl. 2023;35(11):8423-8436. doi: 10.1007/s00521-022-08115-2. Epub 2022 Dec 16. Neural Comput Appl. 2023. PMID: 36568475 Free PMC article. - Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.
Ajala S, Muraleedharan Jalajamony H, Nair M, Marimuthu P, Fernandez RE. Ajala S, et al. Sci Rep. 2022 Jul 13;12(1):11971. doi: 10.1038/s41598-022-16114-5. Sci Rep. 2022. PMID: 35831342 Free PMC article. - CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network.
Suganyadevi S, Seethalakshmi V. Suganyadevi S, et al. Wirel Pers Commun. 2022;126(4):3279-3303. doi: 10.1007/s11277-022-09864-y. Epub 2022 Jun 19. Wirel Pers Commun. 2022. PMID: 35756172 Free PMC article. - A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation.
Kweon J, Yoo J, Kim S, Won J, Kwon S. Kweon J, et al. Sensors (Basel). 2022 May 23;22(10):3960. doi: 10.3390/s22103960. Sensors (Basel). 2022. PMID: 35632368 Free PMC article.
References
- Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. TBME. 2010;57(4):841–]852. - PubMed
- Arteta C, Lempitsky V, Noble JA, Zisserman A. Learning to detect cells using non-overlapping extremal regions. MICCAI. 2012;7510:348–]356. - PubMed
- Byun J, Verardo MR, Sumengen B, Lewis G, Manjunath BS, Fisher SK. Automated tool for the detection of cell nuclei in digital microscopic images: application to retinal images. Mol. Vis. 2006;12:949–]960. - PubMed
- Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. MICCAI. 2013;8150:411–]418. - PubMed
- Ciresan D, Giusti A, Gambardella L. Schmidhuber: Deep neural networks segment neuronal membranes in electron microscopy images. NIPS. 2012:2852–]2860.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources