uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (6 of 6) Show all publications
Gupta, A., Harrison, P. J., Wieslander, H., Pielawski, N., Kartasalo, K., Partel, G., . . . Wählby, C. (2019). Deep Learning in Image Cytometry: A Review. Cytometry Part A, 95(6), 366-380
Open this publication in new window or tab >>Deep Learning in Image Cytometry: A Review
Show others...
2019 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 95, no 6, p. 366-380Article, review/survey (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-371631 (URN)10.1002/cyto.a.23701 (DOI)000466792700002 ()30565841 (PubMedID)
Funder
Swedish Foundation for Strategic Research , BD15-0008SB16-0046Swedish Research Council, 2014-6075EU, European Research Council, ERC-2015-CoG 683810
Available from: 2018-12-19 Created: 2018-12-21 Last updated: 2019-06-14Bibliographically approved
Suveer, A., Gupta, A., Kylberg, G. & Sintorn, I.-M. (2019). Super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019): . Paper presented at 16th IEEE International Symposium on Biomedical Imaging (ISBI), APR 08-11, 2019, Venice, ITALY (pp. 548-551). IEEE
Open this publication in new window or tab >>Super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning
2019 (English)In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, 2019, p. 548-551Conference 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 x 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.

Place, publisher, year, edition, pages
IEEE, 2019
Series
Biomedical Imaging, IEEE International Symposium on, E-ISSN 1945-7928
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-381797 (URN)10.1109/ISBI.2019.8759153 (DOI)000485040000121 ()978-1-5386-3641-1 (ISBN)
Conference
16th IEEE International Symposium on Biomedical Imaging (ISBI), APR 08-11, 2019, Venice, ITALY
Funder
Swedish Foundation for Strategic Research , SB16-0046Vinnova, 2016-02329
Available from: 2019-04-13 Created: 2019-04-13 Last updated: 2019-10-23Bibliographically approved
Gupta, A., Saar, T., Martens, O. & Le Moullec, Y. (2018). Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step. Medical physics (Lancaster), 45(3), 1135-1149
Open this publication in new window or tab >>Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step
2018 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, no 3, p. 1135-1149Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-366999 (URN)10.1002/mp.12746 (DOI)29359462 (PubMedID)
Funder
EU, Horizon 2020, 668995
Available from: 2018-01-23 Created: 2018-11-27 Last updated: 2018-12-04Bibliographically approved
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
Show others...
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
Show others...
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
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
Show others...
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) Clinical Laboratory Medicine
Research subject
Computerized Image Processing; Pathology
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: 2020-01-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3557-4947

Search in DiVA

Show all publications