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Sintorn, Ida-Maria
Publications (10 of 62) Show all publications
Adler, J., Sintorn, I.-M., Strand, R. & Parmryd, I. (2019). Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous. Communications Biology, 2, Article ID 12.
Open this publication in new window or tab >>Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous
2019 (English)In: Communications Biology, ISSN 2399-3642, Vol. 2, article id 12Article in journal (Refereed) Published
National Category
Biophysics
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-380506 (URN)10.1038/s42003-018-0240-2 (DOI)000461148000001 ()30652124 (PubMedID)
Available from: 2019-01-08 Created: 2019-04-15 Last updated: 2019-05-07Bibliographically approved
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
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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
Gupta, A., Saar, T., Martens, O., Le Moullec, Y. & Sintorn, I.-M. (2019). Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks. International journal of imaging systems and technology (Print)
Open this publication in new window or tab >>Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks
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2019 (English)In: International journal of imaging systems and technology (Print), ISSN 0899-9457, E-ISSN 1098-1098, ISSN 0899-9457Article in journal (Refereed) Published
Abstract [en]

Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error‐prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer‐assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidate‐screening module and a false positive (FP) reduction module. The candidate‐screening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features‐based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro‐nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well‐suited FP reduction module of a CAD scheme.

National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-403431 (URN)10.1002/ima.22373 (DOI)
Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-01-31Bibliographically approved
Matuszewski, D. J. & Sintorn, I.-M. (2019). Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images. Computer Methods and Programs in Biomedicine, 178, 31-39
Open this publication in new window or tab >>Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images
2019 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 178, p. 31-39Article in journal (Refereed) Published
Abstract [en]

Background and objective: Convolutional neural networks (CNNs) offer human experts-like performance and in the same time they are faster and more consistent in their prediction. However, most of the proposed CNNs require an expensive state-of-the-art hardware which substantially limits their use in practical scenarios and commercial systems, especially for clinical, biomedical and other applications that require on-the-fly analysis. In this paper, we investigate the possibility of making CNNs lighter by parametrizing the architecture and decreasing the number of trainable weights of a popular CNN: U-Net. Methods: In order to demonstrate that comparable results can be achieved with substantially less trainable weights than the original U-Net we used a challenging application of a pixel-wise virus classification in Transmission Electron Microscopy images with minimal annotations (i.e. consisting only of the virus particle centers or centerlines). We explored 4 U-Net hyper-parameters: the number of base feature maps, the feature maps multiplier, the number of the encoding-decoding levels and the number of feature maps in the last 2 convolutional layers. Results: Our experiments lead to two main conclusions: 1) the architecture hyper-parameters are pivotal if less trainable weights are to be used, and 2) if there is no restriction on the trainable weights number using a deeper network generally gives better results. However, training larger networks takes longer, typically requires more data and such networks are also more prone to overfitting. Our best model achieved an accuracy of 82.2% which is similar to the original U-Net while using nearly 4 times less trainable weights (7.8 M in comparison to 31.0 M). We also present a network with < 2M trainable weights that achieved an accuracy of 76.4%. Conclusions: The proposed U-Net hyper-parameter exploration can be adapted to other CNNs and other applications. It allows a comprehensive CNN architecture designing with the aim of a more efficient trainable weight use. Making the networks faster and lighter is crucial for their implementation in many practical applications. In addition, a lighter network ought to be less prone to over-fitting and hence generalize better. (C) 2019 Published by Elsevier B.V.

Place, publisher, year, edition, pages
ELSEVIER IRELAND LTD, 2019
Keywords
Deep learning, Hyper parameter optimization, Hardware integration, Transmission Electron Microscopy
National Category
Computer Engineering
Identifiers
urn:nbn:se:uu:diva-393644 (URN)10.1016/j.cmpb.2019.05.026 (DOI)000480432000004 ()31416558 (PubMedID)
Funder
Swedish Research Council, 2014-6075
Available from: 2019-09-26 Created: 2019-09-26 Last updated: 2019-10-09Bibliographically 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
Wetzer, E., Lindblad, J., Sintorn, I.-M., Hultenby, K. & Sladoje, N. (2019). Towards automated multiscale Glomeruli detection and analysis in TEM by fusion of CNN and LBP maps. In: 3rd NEUBIAS Conference: . Paper presented at 3rd NEUBIAS Conference. Luxembourg
Open this publication in new window or tab >>Towards automated multiscale Glomeruli detection and analysis in TEM by fusion of CNN and LBP maps
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2019 (English)In: 3rd NEUBIAS Conference, Luxembourg, 2019Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

Glomeruli are special structures in kidneys which filter the plasma volume from metabolic waste.Podocytes are cells that wrap around the capillaries of the Glomerulus. They take an active role in the renal filtration by preventing plasma proteins from entering the urinary ultrafiltrate through slits between so called foot processes. A number of diseases, such as minimal change disease, systemic lupus and diabetic nephropathy, can affect the glomerulus and have serious impact on the kidneys and their function.When the resolution of optical microscopy is insufficient for a diagnosis, it is necessary to thoroughly examine the morphology of the podocytes in transmission electron microscopy (TEM). This includes measuring the size and shape of the foot processes, the thickness and overall morphology of the Glomerulus Base Membrane (GBM), and the number of slits along the GBM.The high resolution of TEM images produces large amounts of data and requires long acquisition time, which makes automated imaging and Glomerulus detection a desired option. We present a multi-step and multi-scale approach to automatically detect Glomeruli and subsequently foot processes by using convolutional neural networks (CNN). Previously, texture information in the form of local binary patterns (LBPs) has shown great success in Glomerulus detection in different modalities other than TEM. This motivates our approach to explore different methods to incorporate LBPs in CNN training to enhance the performance over training exclusively on intensity images. We use a modified approximation of the Earth mover’s distance to define dissimilarities between the initially unordered binary codes resulting from pixel-wise LBP computations.Multidimensional scaling based on those dissimilarities can be applied to compute LBP maps which are suitable as CNN input. We explore the effect of different radii and dimensions for the LBP maps generation, as well as the impact of early, mid and late fusion of intensity and texture information input. We compare the performance of ResNet50 and VGG16-like architectures. Furthermore we provide comparison of transfer learning of networks pretrained on ImageNet, as well as on a publicly available SEM database, a network architecture in which convolutional layers are replaced by local binary convolutional layers, as well as ‘classic’ methods such as SVM or 1-NN classification based on LBP histograms.We show that for Glomerulus detection, where texture is a main discriminative feature, CNN training on the texture based input provides complementary information not learned by the network on the intensity images and mid and late fusion can boost performance. In foot process detection, in which the scale shifts the focus from texture to morphology, the performance also benefits by the handcrafted texture features, though to a lesser extent than for the larger scale Glomerulus detection.

Place, publisher, year, edition, pages
Luxembourg: , 2019
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-398148 (URN)
Conference
3rd NEUBIAS Conference
Available from: 2019-12-02 Created: 2019-12-02 Last updated: 2019-12-04
Hast, A., Sablina, V. A., Sintorn, I.-M. & Kylberg, G. (2018). A fast Fourier based feature descriptor and a cascade nearest neighbour search with an efficient matching pipeline for mosaicing of microscopy images. Pattern Recognition and Image Analysis, 28(2), 261-272
Open this publication in new window or tab >>A fast Fourier based feature descriptor and a cascade nearest neighbour search with an efficient matching pipeline for mosaicing of microscopy images
2018 (English)In: Pattern Recognition and Image Analysis, ISSN 1054-6618, Vol. 28, no 2, p. 261-272Article in journal (Refereed) Published
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-354147 (URN)10.1134/S1054661818020050 (DOI)
Available from: 2018-06-16 Created: 2018-06-19 Last updated: 2018-06-20Bibliographically 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
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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
Matuszewski, D. J., Wählby, C., Krona, C., Nelander, S. & Sintorn, I.-M. (2018). Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma. SLAS Discovery: Advancing Life Sciences R&D, 23(10), 1030-1039
Open this publication in new window or tab >>Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma
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2018 (English)In: SLAS Discovery: Advancing Life Sciences R&D, ISSN 2472-5552, Vol. 23, no 10, p. 1030-1039Article in journal (Refereed) Published
Abstract [en]

Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.

National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368698 (URN)10.1177/2472555218791414 (DOI)000452283500003 ()30074852 (PubMedID)
Funder
AstraZenecaSwedish Research Council, 2012-4968; 2014-6075eSSENCE - An eScience Collaboration
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-10-09Bibliographically approved
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