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Kecheril Sadanandan, SajithORCID iD iconorcid.org/0000-0002-5129-530X
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Publications (9 of 9) Show all publications
Kecheril Sadanandan, S., Ranefall, P., Le Guyader, S. & Wählby, C. (2017). Automated training of deep convolutional neural networks for cell segmentation. Scientific Reports, 7, Article ID 7860.
Open this publication in new window or tab >>Automated training of deep convolutional neural networks for cell segmentation
2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, 7860Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329301 (URN)10.1038/s41598-017-07599-6 (DOI)28798336 (PubMedID)
Funder
Swedish Research Council, 2012-4968EU, European Research Council, 682810
Available from: 2017-08-10 Created: 2017-09-12 Last updated: 2017-11-17
Wieslander, H., Forslid, G., Bengtsson, E., Wählby, C., Hirsch, J.-M., Runow Stark, C. & Kecheril Sadanandan, S. (2017). Deep convolutional neural networks for detecting cellular changes due to malignancy. In: IEEE International Conference on Computer Vision: . Paper presented at ICCV workshop on Bioimage Computing, Venice, Italy, October 23, 2017.. .
Open this publication in new window or tab >>Deep convolutional neural networks for detecting cellular changes due to malignancy
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2017 (English)In: IEEE International Conference on Computer Vision, 2017Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329356 (URN)
Conference
ICCV workshop on Bioimage Computing, Venice, Italy, October 23, 2017.
Funder
EU, European Research Council, 682810Swedish Research Council, 2012-4968
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2017-11-17
Ishaq, O., Sadanandan, S. K. & Wählby, C. (2017). Deep Fish: Deep Learning-Based Classification of Zebrafish Deformation for High-Throughput Screening. Journal of Biomolecular Screening, 22(1), 102-107.
Open this publication in new window or tab >>Deep Fish: Deep Learning-Based Classification of Zebrafish Deformation for High-Throughput Screening
2017 (English)In: Journal of Biomolecular Screening, ISSN 1087-0571, E-ISSN 1552-454X, Vol. 22, no 1, 102-107 p.Article in journal (Refereed) Published
Abstract [en]

Zebrafish (Danio rerio) is an important vertebrate model organism in biomedical research, especially suitable for morphological screening due to its transparent body during early development. Deep learning has emerged as a dominant paradigm for data analysis and found a number of applications in computer vision and image analysis. Here we demonstrate the potential of a deep learning approach for accurate high-throughput classification of whole-body zebrafish deformations in multifish microwell plates. Deep learning uses the raw image data as an input, without the need of expert knowledge for feature design or optimization of the segmentation parameters. We trained the deep learning classifier on as few as 84 images (before data augmentation) and achieved a classification accuracy of 92.8% on an unseen test data set that is comparable to the previous state of the art (95%) based on user-specified segmentation and deformation metrics. Ablation studies by digitally removing whole fish or parts of the fish from the images revealed that the classifier learned discriminative features from the image foreground, and we observed that the deformations of the head region, rather than the visually apparent bent tail, were more important for good classification performance.

National Category
Signal Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-309535 (URN)10.1177/1087057116667894 (DOI)000394206000012 ()27613194 (PubMedID)
Funder
Swedish Research Council, 2012-4968eSSENCE - An eScience Collaboration
Available from: 2016-12-05 Created: 2016-12-05 Last updated: 2017-11-17
Kecheril Sadanandan, S., Karlsson, J. & Wählby, C. (2017). Spheroid segmentation using multiscale deep adversarial networks. In: IEEE International Conference on Computer Vision: . Paper presented at ICCV Workshop on Bioimage Computing, Venice, Italy, October 23, 2017.. .
Open this publication in new window or tab >>Spheroid segmentation using multiscale deep adversarial networks
2017 (English)In: IEEE International Conference on Computer Vision, 2017Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329355 (URN)
Conference
ICCV Workshop on Bioimage Computing, Venice, Italy, October 23, 2017.
Funder
Swedish Research Council, 2012-4968EU, European Research Council, 682810
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2017-11-17
Ranefall, P., Sadanandan, S. K. & Wählby, C. (2016). Fast Adaptive Local Thresholding Based on Ellipse fit. In: : . Paper presented at International Symposium on Biomedical Imaging (ISBI'16), Prague, Czech Republic, April 13-16, 2016. .
Open this publication in new window or tab >>Fast Adaptive Local Thresholding Based on Ellipse fit
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we propose an adaptive thresholding method where each object is thresholded optimizing its shape. The method is based on a component tree representation, which can be computed in quasi-linear time. We test and evaluate the method on images of bacteria from three different live-cell analysis experiments and show that the proposed method produces segmentation results comparable to state-of-the-art but at least an order of magnitude faster. The method can be extended to compute any feature measurements that can be calculated in a cumulative way, and holds great potential for applications where a priori information on expected object size and shape is available.

Series
Scripta minora Bibliothecae regiae Universitatis Upsaliensis, ISSN 0282-3152
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-275182 (URN)000386377400049 ()9781479923496 (ISBN)9781479923502 (ISBN)
Conference
International Symposium on Biomedical Imaging (ISBI'16), Prague, Czech Republic, April 13-16, 2016
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish Research Council, 2012-4968
Available from: 2016-02-01 Created: 2016-02-01 Last updated: 2017-09-21Bibliographically approved
Kecheril Sadanandan, S., Ranefall, P. & Wählby, C. (2016). Feature augmented deep neural networks for segmentation of cells. In: Hua, Gang; Jégou, Hervé (Ed.), Computer Vision – ECCV 2016 Workshops: Part I. Paper presented at IEEE International Conference on Computer Vision (ICCV) (pp. 231-243). Springer.
Open this publication in new window or tab >>Feature augmented deep neural networks for segmentation of cells
2016 (English)In: Computer Vision – ECCV 2016 Workshops: Part I, Springer, 2016, 231-243 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science, 9913
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-306914 (URN)10.1007/978-3-319-46604-0_17 (DOI)978-3-319-46603-3 (ISBN)
Conference
IEEE International Conference on Computer Vision (ICCV)
Funder
Swedish Research Council, 2012-4968
Available from: 2016-09-18 Created: 2016-11-05 Last updated: 2017-11-17
Ranefall, P., Sadanandan, S. K. & Wählby, C. (2016). Global And Local Adaptive Gray-level Thresholding Based on Object Features. In: Robin Strand (Ed.), Swedish Symposium on Image Analysis 2016: . Paper presented at Swedish Symposium on Image Analysis 2016 (SSBA 2016) March 14-16 2016, Uppsala, Sweden. .
Open this publication in new window or tab >>Global And Local Adaptive Gray-level Thresholding Based on Object Features
2016 (English)In: Swedish Symposium on Image Analysis 2016 / [ed] Robin Strand, 2016Conference paper, Oral presentation only (Other academic)
Abstract [en]

In this paper we propose a) a fast and robustglobal gray-level thresholding method based on object size,where the selection of threshold level is based on recall andmaximum precision with regard to objects within a givensize interval, and b) an adaptive thresholding method whereeach object is thresholded optimizing its shape. The methodsare based on on the component tree representation, whichcan be computed in quasi-linear time. We show that forreal images of cell nuclei and synthetic data sets mimickingfluorescent spots the proposed methods are more robust thanall standard global thresholding methods in ImageJ andCellProfiler. The methods can be extended to compute anyfeature measurements that can be calculated in a cumulativeway, and hold great potential for applications where a prioriinformation on expected object size and shape is available.

National Category
Other Computer and Information Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-315728 (URN)
Conference
Swedish Symposium on Image Analysis 2016 (SSBA 2016) March 14-16 2016, Uppsala, Sweden
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Available from: 2017-02-20 Created: 2017-02-20 Last updated: 2017-02-23Bibliographically approved
Kecheril Sadanandan, S., Baltekin, Ö., Magnusson, K. E. G., Boucharin, A., Ranefall, P., Jaldén, J., . . . Wählby, C. (2016). Segmentation and track-analysis in time-lapse imaging of bacteria. IEEE Journal on Selected Topics in Signal Processing, 10(1), 174-184.
Open this publication in new window or tab >>Segmentation and track-analysis in time-lapse imaging of bacteria
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2016 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 10, no 1, 174-184 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we have developed tools to analyze prokaryotic cells growing in monolayers in a microfluidic device. Individual bacterial cells are identified using a novel curvature based approach and tracked over time for several generations. The resulting tracks are thereafter assessed and filtered based on track quality for subsequent analysis of bacterial growth rates. The proposed method performs comparable to the state-of-the-art methods for segmenting phase contrast and fluorescent images, and we show a 10-fold increase in analysis speed.

Keyword
E. coli; microscopy; segmentation; time-lapse; tracking
National Category
Bioinformatics (Computational Biology)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-265457 (URN)10.1109/JSTSP.2015.2491304 (DOI)000369495900015 ()
Projects
eSSENCE
Funder
Swedish Research Council, 2012-4968EU, European Research Council, 616047eSSENCE - An eScience Collaboration
Available from: 2016-01-21 Created: 2015-10-29 Last updated: 2017-11-17
Kecheril Sadanandan, S. & Wählby, C. (2013). Large-Scale Analysis of Live Cells. In: : . Lund University.
Open this publication in new window or tab >>Large-Scale Analysis of Live Cells
2013 (English)Conference paper, Poster (with or without abstract) (Other (popular science, discussion, etc.))
Place, publisher, year, edition, pages
Lund University: , 2013
Keyword
Cell tracking, cell segmentation
National Category
Computer and Information Science
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-209650 (URN)
Funder
eSSENCE - An eScience CollaborationScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Available from: 2013-10-23 Created: 2013-10-23 Last updated: 2013-12-12
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5129-530X

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