Overlapping Cell Nuclei Segmentation in Microscopic Images … · 2020. 1. 18. · Original Image...
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Original Image Thresholding k-means Arslan et al. (2014) Proposed Method Overlapping Cell Nuclei Segmentation in Microscopic Images Using Deep Belief Networks 1 Rahul Duggal, 1 Anubha Gupta, 2 Ritu Gupta, 1 Manya Wadhwa , and 1 Chirag Ahuja Problem Motivation Qualitative Results Proposed Method Quantitative Results Automated image analysis of cells and tissues is an active research topic in the field of medical informatics. A common problem encountered in the field is cell classification. This requires one to operate on individual cells. Since WBC nuclei usually occur in clusters (shown above), the first step in any such study requires segmentation of individual nuclei from these clusters. Apply k-means clustering in ‘a’ and ‘b’ channels of Lab space Apply distance transform and locate minima Is there any CC with >1 minimum? Break clusters using Deep Learning Output segmented image Determine connected components (CC) Binary nucleus mask No 1 SBILab, Dept. of E.C.E, IIIT-Delhi, 2 AIIMS, New Delhi Method Avg. Time per Image (sec) Segmented Individual cells Clusters Total - 151 31 Thresholding 0.217 78 0 k-means 6.4982 122 0 Arslan et al. (2014) 327.54 126 28 Proposed DBN-4 40.98 151 28 Method Avg. Time per image (sec) TPR FDR F-Score Arslan et al. (2014) 327.55 0.93 0.27 0.81 Proposed DBN-4 40.98 0.97 0.16 0.89 Acknowledgement Communication and IT, Govt of India for this research work. Table 2: Comparative performance of different methods Table 3: Comparison on number of nuclei segmented Yes If the set of pixels connecting two overlapping WBC nuclei could be determined accurately, we can break the cluster by dropping them. We train a 4-layer Deep Belief Network to identify these “ridge” pixels. Key Idea Ridge Pixels Train a DBN to classify pixels among three classes Drop pixels belonging to the ridge (blue) class Authors gratefully acknowledge the research funding support (Grant Number: 1(7)/2014- ME&HI) from the Ministry of Segmentation Result References Table 1: Results on 2 images with different methods [1] H. Larochelle, Y. Bengio, J.Louradour, and P. Lamblin. Exploring strategies for training deep neural networks. Journal of Machine Learning Research, 10(1),1- 40, 2009. [2] G. E. Hinton, S. Osindero, and Y.-W. Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7), pp. 1527-1554, 2006. [3] S. Arslan, E. Ozyurek, and C. Gunduz- Demir. A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images. Cytometry Part A, 85(6),480-490, 2014.
Transcript of Overlapping Cell Nuclei Segmentation in Microscopic Images … · 2020. 1. 18. · Original Image...
Original Image Thresholding k-means Arslan et al. (2014) Proposed Method
Overlapping Cell Nuclei Segmentation in Microscopic Images Using Deep Belief Networks1Rahul Duggal, 1Anubha Gupta, 2Ritu Gupta, 1Manya Wadhwa, and 1Chirag Ahuja
Problem Motivation
Qualitative Results
Proposed Method
Quantitative Results
Automated image analysis of cells and tissues is an activeresearch topic in the field of medical informatics. Acommon problem encountered in the field is cellclassification. This requires one to operate on individualcells. Since WBC nuclei usually occur in clusters (shownabove), the first step in any such study requiressegmentation of individual nuclei from these clusters.
Apply k-means clustering in ‘a’ and ‘b’ channels of Lab space
Apply distance transform and locate minima
Is there any CC with >1
minimum?Break clusters using Deep
Learning
Output segmented image
Determine connected components (CC)
Binary nucleus mask
No
1SBILab, Dept. of E.C.E, IIIT-Delhi, 2AIIMS, New Delhi
MethodAvg. Time per
Image (sec)
SegmentedIndividual
cellsClusters
Total - 151 31Thresholding 0.217 78 0k-means 6.4982 122 0
Arslan et al. (2014) 327.54 126 28
Proposed DBN-4 40.98 151 28
MethodAvg. Time per
image (sec)TPR FDR F-Score
Arslan et al. (2014) 327.55 0.93 0.27 0.81
Proposed DBN-4 40.98 0.97 0.16 0.89
Acknowledgement
Communication and IT, Govt of India for this research work.
Table 2: Comparative performance of different methods
Table 3: Comparison on number of nuclei segmented
Yes
If the set of pixels connecting two overlapping WBC nucleicould be determined accurately, we can break the clusterby dropping them. We train a 4-layer Deep Belief Networkto identify these “ridge” pixels.
Key Idea
Ridge PixelsTrain a DBN to classify pixels among three
classes
Drop pixels belonging
to the ridge (blue) class
Authors gratefully acknowledge the researchfunding support (Grant Number: 1(7)/2014-ME&HI) from the Ministry of
Segmentation ResultReferences
Table 1: Results on 2 images with different methods
[1] H. Larochelle, Y. Bengio, J.Louradour, and P. Lamblin. Exploring strategies for training deep neural networks. Journal of Machine Learning Research, 10(1),1-40, 2009.
[2] G. E. Hinton, S. Osindero, and Y.-W. Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7), pp. 1527-1554, 2006.
[3] S. Arslan, E. Ozyurek, and C. Gunduz-Demir. A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images. Cytometry Part A, 85(6),480-490, 2014.
