Agenda
Overview
Technical Details
Our Team
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Overview
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Simple task
Nuclei segmentation
Overview
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Train
Test
Input images sample variability
Overview
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Differing Saliency
- some nucleus can be easily seen
- some are barely distinguishable
- some have homogenous structure
- some have nucleous in the center
Overview
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Varying shape and size
- some are elliptical
- some are oblong
- some are large
- some are very small
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Overview
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Varying level of nuclei attachment
- some images are very dense
- some are sparse
- some have large groups of glued nuclei
Pipeline
Input image
Preprocessing
Morphological pooling
Watershed ensemble generation
Thresholding
Morphological ensemble generation
Technical Details
Watershed pooling
Segmented mask
Preprocessing
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Hematoxylin
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Eosin
Background
Technical Details
Color deconvolution
Pipeline
Preprocessing
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Hematoxylin
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Eosin
Background
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Hematoxylin Channel Extraction
Technical Details
Color deconvolution
Pipeline
Preprocessing
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Reinhard's Color Normalization
References:
- Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Computer Graphics and Applications 21(5) (2001) 34–41
- Wang, Y., nd L. Wu, S.C., Tsai, S., Sun, Y.: A color-based approach for automated segmentation in tumor tissue classification. In: Proc. Conf. of the IEEE Engineering in Medicine and Biology Society. (2007) 6577–6580
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Target image
Technical Details
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Pipeline
Preprocessing
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Hematoxylin Channel Extraction
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Reinhard's Color Normalization
Target image
Technical Details
Pipeline
Thresholding
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Possible algorithms:
- K-means clustering
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Fuzzy c-means clustering
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Gaussian mixture models
2. With spatial information
1. No spatial information
- Spatial information Fuzzy c-means clustering
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Fast spatial distance weighted Fuzzy c-means clustering
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Dictionary model
References:
- Zeng, Huang, Kang and Sang. Image segmentation using spectral clustering of Gaussian mixture models. Neurocomputing 144:346–356, 2014.
- Guo, Liu, Wu, Hong and Zhang. A New Spatial Fuzzy C-Means for Spatial Clustering. WSEAS Transactions on Computers 14:369-381, 2015.
- Dahl and Larsen. Learning Dictionaries of Discriminative Image Patches. In: Proc. British Machine Vision Conference. p.77, 2011.
- Hamed Shamsi and Hadi Seyedarabi, Member, IACSITA Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation,International Journal of Computer Theory and Engineering, Vol. 4, No. 5, 2012.
Technical Details
Pipeline
Thresholding
K-means clustering
Parameters:
- number of color clusters
- number of most most intensive clusters to be classified as nuclei
Models
- k-means(3,1)
- k-means(4,2)
- k-means(6,3)
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input
color clusters
1
6
5
4
3
2
Technical Details
Pipeline
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Thresholding
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input
color clusters
top 3
most intensive clusters sum
1
6
5
4
3
2
4+5+6
K-means clustering
Parameters:
- number of color clusters
- number of most most intensive clusters to be classified as nuclei
Models
- k-means(3,1)
- k-means(4,2)
- k-means(6,3)
Technical Details
Pipeline
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Thresholding
Gaussian Mixture Model
Parameters:
- number of color clusters
- number of most most intensive clusters to be classified as nuclei
- which of the distribution parameters (means, covariances,weights) should be updated
- which covariance option (diagonal,full,tied,spherical) should be used
Models
- GMM(3,1,{means,covariances},diagonal)
- GMM(6,3,{covariances},diagonal)
- GMM(6,3,{covariances},tied)
References:
- Zeng, Huang, Kang and Sang. Image segmentation using spectral clustering of Gaussian mixture models. Neurocomputing 144:346–356, 2014.
Technical Details
Pipeline
Thresholding
Spatial distance weighted fuzzy c-means clustering
Parameters:
- number of color clusters
- number of most most intensive clusters to be classified as nuclei
- fuzziness parameter
- spatial information importance
- number of neighbours
Models
- SDWFCM(3,1,2,0.9,8)
- SDWFCM(3,1,2,0.9,24)
- SDWFCM(3,2,2,0.9,24)
cluster center
spatial distance weighted funcion
weighted distance
membership probabilty
objective function
References:
- Hamed Shamsi and Hadi Seyedarabi, Member, IACSITA Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation,International Journal of Computer Theory and Engineering, Vol. 4, No. 5, 2012.
Technical Details
Pipeline
Thresholding
Dictionary model
References:
- Dahl and Larsen. Learning Dictionaries of Discriminative Image Patches. In: Proc. British Machine Vision Conference. p.77, 2011.
Technical Details
Learning:
- seeding dictionaries of texture and label patches
- iterative clustering of patches according to label similarity
Segmentation:
- assigning the idealized label of the most similar cluster centroid
Pipeline
Thresholding
Parameters:
- patch size
- label similarity threshold
- percentage of seeding and for training
- number of training iterations
- adjustment coefficient for centroids of texture clusters
Models
- DM(1,0.4,0.01,0.1,5,0.05,0.5)
- DM(1,0.5,0.01,0.1,20,0.05,0.0)
- DM(2,0.6,0.01,0.1,5,0.05,0.0)
- DM(3,0.6,0.01,0.1,5,0.15,0.5)
References:
- Dahl and Larsen. Learning Dictionaries of Discriminative Image Patches. In: Proc. British Machine Vision Conference. p.77, 2011.
Technical Details
Dictionary model
Pipeline
Thresholding
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Reality is far from perfect:
- leftover artifacts
- hollow shapes
- glued nuclei groups
Technical Details
Pipeline
Thresholding
Operations:
- dilation and erosion
- reconstruction by erosion
- closing and opening
- fill binary holes
Morphological Transformations
Technical Details
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Pipeline
Thresholding
Operations:
- dilation and erosion
- reconstruction by erosion
- closing and opening
- fill binary holes
Morphological Transformations
Problems:
- Sample diversity
- Diversity within one image
- "one size fits all" structuring element does not exist
Technical Details
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Pipeline
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Morphological ensemble generation
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k-means(3,1)
k-means(4,2)
SDWFCM(3,2,2,0.9,24)
1
2
r
Morphological ensemble
Technical Details
Pipeline
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k-means(3,1)
k-means(4,2)
SDWFCM(3,2,2,0.9,24)
1
2
r
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Morphological ensemble
Morphological pooling
probabilty map
Technical Details
Pipeline
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k-means(3,1)
k-means(4,2)
SDWFCM(3,2,2,0.9,24)
1
2
r
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Morphological ensemble
k-means clustering
Morphological pooling
probabilty map
Technical Details
Pipeline
nuclei detachment
Watershed ensemble generation
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Watershed algorithm
- local maxima search space
- minimal distance between markers
- other
Technical Details
Pipeline
nuclei detachment
Watershed ensemble generation
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Watershed algorithm
- local maxima search space
- minimal distance between markers
- other
Problems:
- Sample diversity
- Diversity within one image
- "one size fits all" parameter set does not exist
Technical Details
Pipeline
Watershed ensemble generation
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watershed realizations
thresholding output
Technical Details
Pipeline
Watershed ensemble generation
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watershed realizations
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thresholding output
edge realizations
Technical Details
Pipeline
Watershed pooling
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probability map
thresholding output
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edge realizations
Technical Details
Pipeline
Watershed pooling
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probability map
thresholding output
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modified thresholding output
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overimpose
edge realizations
Technical Details
Pipeline
Watershed pooling
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probability map
thresholding output
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modified thresholding output
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final segmentation mask
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watershed
overimpose
edge realizations
Technical Details
Pipeline
Our Team
Grzegorz Żurek
R&D Stermedia
Wroclaw University of Technology
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Jakub Czakon
R&D Stermedia
Piotr Giedziun
R&D Stermedia
Ph.D Witold Dyrka
R&D Stermedia
Wroclaw University of Technology
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Piotr Krajewski
CIO Stermedia
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Michał Błach
R&D Stermedia
Wroclaw University of Technology
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MD Łukasz Fuławka
Patomorphology resident
Lower Silesian Oncology Center
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Contact us
info@stermedia.pl
jakub.czakon@stermedia.pl
grzegorz.zurek@stermedia.pl
We are looking forward to collaborating with you
Thank you for attention
Segmentation Algorithm
By Stermedia Sp. z o.o.
Segmentation Algorithm
presentation for MICCAI 2015
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