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Suveer, Amit
Publications (10 of 13) Show all publications
Suveer, A. (2019). Super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning. In: : . Paper presented at International Symposium on Biomedical Imaging.
Open this publication in new window or tab >>Super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning
2019 (English)Conference paper, Published paper (Refereed)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-381796 (URN)
Conference
International Symposium on Biomedical Imaging
Available from: 2019-04-13 Created: 2019-04-13 Last updated: 2019-04-13
Suveer, A. (2019). super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning. In: : . Paper presented at International Symposium on Biomedical Imaging.
Open this publication in new window or tab >>super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Deep learning techniques have shown promising outcomes

in single image super-resolution (SR) reconstruction

from noisy and blurry low resolution data. The SR reconstruction

can cater the fundamental limitations of transmission

electron microscopy (TEM) imaging to potentially attain

a balance among the trade-offs like imaging-speed, spatial/

temporal resolution, and dose/exposure-time, which is

often difficult to achieve simultaneously otherwise. In this

work, we present a convolutional neural network (CNN)

model, utilizing both local and global skip connections, aiming

for 4 SR reconstruction of TEM images. We used exact

image pairs of a calibration grid to generate our training and

independent testing datasets. The results are compared and

discussed using models trained on synthetic (downsampled)

and real data from the calibration grid. We also compare the

variants of the proposed network with well-known classical

interpolations techniques. Finally, we investigate the domain

adaptation capacity of the CNN-based model by testing it

on TEM images of a cilia sample, having different image

characteristics as compared to the calibration-grid.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-381797 (URN)
Conference
International Symposium on Biomedical Imaging
Available from: 2019-04-13 Created: 2019-04-13 Last updated: 2019-04-17
Gupta, A., Harrison, P. J., Wieslander, H., Pielawski, N., Kartasalo, K., Partel, G., . . . Wählby, C. (2018). Deep Learning in Image Cytometry: A Review.. Cytometry Part A
Open this publication in new window or tab >>Deep Learning in Image Cytometry: A Review.
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2018 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930Article in journal (Refereed) Epub ahead of print
Abstract [en]

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Keywords
biomedical image analysis, cell analysis, convolutional neural networks, deep learning, image cytometry, machine learning, microscopy
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-371631 (URN)10.1002/cyto.a.23701 (DOI)30565841 (PubMedID)
Funder
Swedish Foundation for Strategic Research
Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2019-03-28
Bajic, B., Suveer, A., Gupta, A., Pepic, I., Lindblad, J., Sladoje, N. & Sintorn, I.-M. (2018). Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement. In: Proc. 15th International Symposium on Biomedical Imaging: . Paper presented at ISBI 2018, April 4–7, Washington, DC (pp. 921-925). IEEE
Open this publication in new window or tab >>Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement
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2018 (English)In: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, p. 921-925Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2018
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367040 (URN)10.1109/ISBI.2018.8363721 (DOI)000455045600210 ()978-1-5386-3636-7 (ISBN)
Conference
ISBI 2018, April 4–7, Washington, DC
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-04-17Bibliographically approved
Gupta, A., Suveer, A., Bajic, B., Pepic, I., Lindblad, J., Sladoje, N. & Sintorn, I.-M. (2018). Denoising of Short Exposure Transmission Electron Microscopy Images using CNN. In: Swedish Symposium on Image Analysis: . Paper presented at SSBA2018, Stockholm, Sweden, March 2018.
Open this publication in new window or tab >>Denoising of Short Exposure Transmission Electron Microscopy Images using CNN
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2018 (English)In: Swedish Symposium on Image Analysis, 2018Conference paper, Published paper (Other academic)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367996 (URN)
Conference
SSBA2018, Stockholm, Sweden, March 2018
Available from: 2018-12-02 Created: 2018-12-02 Last updated: 2019-03-06Bibliographically approved
Suveer, A., Sladoje, N., Lindblad, J., Dragomir, A. & Sintorn, I.-M. (2017). Cilia ultrastructural visibility enhancement by multiple instance registration and super-resolution reconstruction. In: Swedish Symposium on Image Analysis: . Swedish Society for Automated Image Analysis
Open this publication in new window or tab >>Cilia ultrastructural visibility enhancement by multiple instance registration and super-resolution reconstruction
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2017 (English)In: Swedish Symposium on Image Analysis, Swedish Society for Automated Image Analysis , 2017Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Swedish Society for Automated Image Analysis, 2017
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-335371 (URN)
Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-08-24
Gupta, A., Suveer, A., Lindblad, J., Dragomir, A., Sintorn, I.-M. & Sladoje, N. (2017). Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images. In: Image Analysis: Part I. Paper presented at SCIA 2017, June 12–14, Tromsø, Norway (pp. 407-418). Springer
Open this publication in new window or tab >>Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images
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2017 (English)In: Image Analysis: Part I, Springer, 2017, p. 407-418Conference paper, Published paper (Refereed)
Abstract [en]

Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10269
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-334218 (URN)10.1007/978-3-319-59126-1_34 (DOI)000454359300034 ()978-3-319-59125-4 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Funder
VINNOVA, 2016-02329
Available from: 2017-05-19 Created: 2017-11-21 Last updated: 2019-04-17Bibliographically approved
Suveer, A., Sladoje, N., Lindblad, J., Dragomir, A. & Sintorn, I.-M. (2017). Enhancement of cilia sub-structures by multiple instance registration and super-resolution reconstruction. In: Image Analysis: Part II. Paper presented at SCIA 2017, June 12–14, Tromsø, Norway (pp. 362-374). Springer
Open this publication in new window or tab >>Enhancement of cilia sub-structures by multiple instance registration and super-resolution reconstruction
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2017 (English)In: Image Analysis: Part II, Springer, 2017, p. 362-374Conference paper, Published paper (Refereed)
Abstract [en]

Ultrastructural analysis of cilia cross-sectional images using transmission electron microscopy (TEM) assists the pathologists to diagnose Primary Ciliary Dyskinesia, a genetic disease. The current diagnostic procedure is manual and difficult because of poor signal-to-noise ratio in TEM images. In this paper, we propose an automated multi-step registration approach to register many cilia cross-sectional instances. The novelty of the work is in the utilization of customized weight masks at each registration step to achieve good alignment of the specific cilium regions. Registration is followed by super-resolution reconstruction to enhance the substructural information. Landmarks matching based evaluation of registration results in pixel alignment error of 2.35±1.82" role="presentation">2.35±1.82 pixels, and the subjective analysis of super-resolution reconstructed cilium shows a clear improvement in the visibility of the substructures such as dynein arms, radial spokes, and central pair.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10270
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-334225 (URN)10.1007/978-3-319-59129-2_31 (DOI)000454360300031 ()978-3-319-59128-5 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Available from: 2017-05-19 Created: 2017-11-21 Last updated: 2019-04-17Bibliographically approved
Gupta, A., Suveer, A., Lindblad, J., Dragomir, A., Sintorn, I.-M. & Sladoje, N. (2017). False positive reduction of cilia detected in low resolution TEM images using a convolutional neural network. In: Swedish Symposium on Image Analysis: . Paper presented at SWEDISH SYMPOSIUM ON IMAGE ANALYSIS 2017 (SSBA), 13-15 March 2017, Linköping, Sweden. Swedish Society for Automated Image Analysis
Open this publication in new window or tab >>False positive reduction of cilia detected in low resolution TEM images using a convolutional neural network
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2017 (English)In: Swedish Symposium on Image Analysis, Swedish Society for Automated Image Analysis , 2017Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Swedish Society for Automated Image Analysis, 2017
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-335454 (URN)
Conference
SWEDISH SYMPOSIUM ON IMAGE ANALYSIS 2017 (SSBA), 13-15 March 2017, Linköping, Sweden
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2018-08-24Bibliographically approved
Suveer, A., Sladoje, N., Lindblad, J., Dragomir, A. & Sintorn, I.-M. (2016). Automated detection of cilia in low magnification transmission electron microscopy images using template matching. In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on: . Paper presented at IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016 (pp. 386-390). IEEE
Open this publication in new window or tab >>Automated detection of cilia in low magnification transmission electron microscopy images using template matching
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2016 (English)In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, IEEE, 2016, p. 386-390Conference paper, Published paper (Other academic)
Abstract [en]

Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.

Place, publisher, year, edition, pages
IEEE, 2016
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keywords
Image resolution, Transmission Electron Microscopy, Object detection, Shape, Image analysis, Template matching
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-308090 (URN)10.1109/ISBI.2016.7493289 (DOI)000386377400093 ()9781479923496 (ISBN)9781479923502 (ISBN)
Conference
IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2019-04-17Bibliographically approved
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