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Kazwiny Y, Pedrosa J, Zhang Z, Boesmans W, D'hooge J, Vanden Berghe P. Extracting neuronal activity signals from microscopy recordings of contractile tissue using B-spline Explicit Active Surfaces (BEAS) cell tracking. Sci Rep 2021; 11:10937. [PMID: 34035411 PMCID: PMC8149687 DOI: 10.1038/s41598-021-90448-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 05/06/2021] [Indexed: 01/13/2023] Open
Abstract
Ca2+ imaging is a widely used microscopy technique to simultaneously study cellular activity in multiple cells. The desired information consists of cell-specific time series of pixel intensity values, in which the fluorescence intensity represents cellular activity. For static scenes, cellular signal extraction is straightforward, however multiple analysis challenges are present in recordings of contractile tissues, like those of the enteric nervous system (ENS). This layer of critical neurons, embedded within the muscle layers of the gut wall, shows optical overlap between neighboring neurons, intensity changes due to cell activity, and constant movement. These challenges reduce the applicability of classical segmentation techniques and traditional stack alignment and regions-of-interest (ROIs) selection workflows. Therefore, a signal extraction method capable of dealing with moving cells and is insensitive to large intensity changes in consecutive frames is needed. Here we propose a b-spline active contour method to delineate and track neuronal cell bodies based on local and global energy terms. We develop both a single as well as a double-contour approach. The latter takes advantage of the appearance of GCaMP expressing cells, and tracks the nucleus' boundaries together with the cytoplasmic contour, providing a stable delineation of neighboring, overlapping cells despite movement and intensity changes. The tracked contours can also serve as landmarks to relocate additional and manually-selected ROIs. This improves the total yield of efficacious cell tracking and allows signal extraction from other cell compartments like neuronal processes. Compared to manual delineation and other segmentation methods, the proposed method can track cells during large tissue deformations and high-intensity changes such as during neuronal firing events, while preserving the shape of the extracted Ca2+ signal. The analysis package represents a significant improvement to available Ca2+ imaging analysis workflows for ENS recordings and other systems where movement challenges traditional Ca2+ signal extraction workflows.
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Affiliation(s)
- Youcef Kazwiny
- Laboratory for Enteric NeuroScience (LENS), Translational Research Center for Gastrointestinal Disorders (TARGID), University of Leuven (KU Leuven), Leuven, Belgium
| | - João Pedrosa
- Laboratory of Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, University of Leuven (KU Leuven), Leuven, Belgium
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, Porto, Portugal
| | - Zhiqing Zhang
- Laboratory for Enteric NeuroScience (LENS), Translational Research Center for Gastrointestinal Disorders (TARGID), University of Leuven (KU Leuven), Leuven, Belgium
| | - Werend Boesmans
- Department of Pathology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
- Biomedical Research Institute (BIOMED), Hasselt University, Hasselt, Belgium
| | - Jan D'hooge
- Laboratory of Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, University of Leuven (KU Leuven), Leuven, Belgium
| | - Pieter Vanden Berghe
- Laboratory for Enteric NeuroScience (LENS), Translational Research Center for Gastrointestinal Disorders (TARGID), University of Leuven (KU Leuven), Leuven, Belgium.
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Borovec J, Kybic J, Arganda-Carreras I, Sorokin DV, Bueno G, Khvostikov AV, Bakas S, Chang EIC, Heldmann S, Kartasalo K, Latonen L, Lotz J, Noga M, Pati S, Punithakumar K, Ruusuvuori P, Skalski A, Tahmasebi N, Valkonen M, Venet L, Wang Y, Weiss N, Wodzinski M, Xiang Y, Xu Y, Yan Y, Yushkevich P, Zhao S, Munoz-Barrutia A. ANHIR: Automatic Non-Rigid Histological Image Registration Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3042-3052. [PMID: 32275587 PMCID: PMC7584382 DOI: 10.1109/tmi.2020.2986331] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.
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Live-Cell Imaging and Analysis of Nuclear Body Mobility. Methods Mol Biol 2020. [PMID: 32681479 DOI: 10.1007/978-1-0716-0763-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
The cell nucleus contains different domains and nuclear bodies, whose position relative to each other inside the nucleus can vary depending on the physiological state of the cell. Changes in the three-dimensional organization are associated with the mobility of individual components of the nucleus. In this chapter, we present a protocol for live-cell imaging and analysis of nuclear body mobility. Unlike other similar protocols, our image analysis pipeline includes non-rigid compensation for global motion of the nucleus before particle tracking and trajectory analysis, leading to precise detection of intranuclear movements. The protocol described can be easily adapted to work with most cell lines and nuclear bodies.
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Paul R, Schabath M, Gillies R, Hall L, Goldgof D. Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future. Comput Biol Med 2020; 122:103882. [PMID: 32658721 DOI: 10.1016/j.compbiomed.2020.103882] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 06/10/2020] [Accepted: 06/22/2020] [Indexed: 01/03/2023]
Abstract
Convolutional Neural Networks (CNNs) have been utilized for to distinguish between benign lung nodules and those that will become malignant. The objective of this study was to use an ensemble of CNNs to predict which baseline nodules would be diagnosed as lung cancer in a second follow up screening after more than one year. Low-dose helical computed tomography images and data were utilized from the National Lung Screening Trial (NLST). The malignant nodules and nodule positive controls were divided into training and test cohorts. T0 nodules were used to predict lung cancer incidence at T1 or T2. To increase the sample size, image augmentation was performed using rotations, flipping, and elastic deformation. Three CNN architectures were designed for malignancy prediction, and each architecture was trained using seven different seeds to create the initial weights. This enabled variability in the CNN models which were combined to generate a robust, more accurate ensemble model. Augmenting images using only rotation and flipping and training with images from T0 yielded the best accuracy to predict lung cancer incidence at T2 from a separate test cohort (Accuracy = 90.29%; AUC = 0.96) based on an ensemble 21 models. Images augmented by rotation and flipping enabled effective learning by increasing the relatively small sample size. Ensemble learning with deep neural networks is a compelling approach that accurately predicted lung cancer incidence at the second screening after the baseline screen mostly 2 years later.
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Affiliation(s)
- Rahul Paul
- Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA.
| | - Matthew Schabath
- Department of Cancer Epidemiology, H. L. Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Robert Gillies
- Department of Cancer Imaging and Metabolism, H. L. Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Lawrence Hall
- Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA
| | - Dmitry Goldgof
- Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA
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Paul R, Schabath MB, Gillies R, Hall LO, Goldgof DB. Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data. J Med Imaging (Bellingham) 2020; 7:024502. [PMID: 32280729 PMCID: PMC7134617 DOI: 10.1117/1.jmi.7.2.024502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 03/09/2020] [Indexed: 12/26/2022] Open
Abstract
Purpose: Due to the high incidence and mortality rates of lung cancer worldwide, early detection of a precancerous lesion is essential. Low-dose computed tomography is a commonly used technique for screening, diagnosis, and prognosis of non-small-cell lung cancer. Recently, convolutional neural networks (CNN) had shown great potential in lung nodule classification. Clinical information (family history, gender, and smoking history) together with nodule size provide information about lung cancer risk. Large nodules have greater risk than small nodules. Approach: A subset of cases from the National Lung Screening Trial was chosen as a dataset in our study. We divided the nodules into large and small nodules based on different clinical guideline thresholds and then analyzed the groups individually. Similarly, we also analyzed clinical features by dividing them into groups. CNNs were designed and trained over each of these groups individually. To our knowledge, this is the first study to incorporate nodule size and clinical features for classification using CNN. We further made a hybrid model using an ensemble with the CNN models of clinical and size information to enhance malignancy prediction. Results: From our study, we obtained 0.9 AUC and 83.12% accuracy, which was a significant improvement over our previous best results. Conclusions: In conclusion, we found that dividing the nodules by size and clinical information for building predictive models resulted in improved malignancy predictions. Our analysis also showed that appropriately integrating clinical information and size groups could further improve risk prediction.
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Affiliation(s)
- Rahul Paul
- University of South Florida, Department of Computer Science and Engineering, Tampa, Florida, United States
| | - Matthew B. Schabath
- H. L. Moffitt Cancer Center and Research Institute, Department of Cancer Epidemiology, Tampa, Florida, United States
| | - Robert Gillies
- H. L. Moffitt Cancer Center and Research Institute, Department of Cancer Physiology, Tampa, Florida, United States
| | - Lawrence O. Hall
- University of South Florida, Department of Computer Science and Engineering, Tampa, Florida, United States
| | - Dmitry B. Goldgof
- University of South Florida, Department of Computer Science and Engineering, Tampa, Florida, United States
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Gao Q, Rohr K. A Global Method for Non-Rigid Registration of Cell Nuclei in Live Cell Time-Lapse Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2259-2270. [PMID: 30835217 DOI: 10.1109/tmi.2019.2901918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Non-rigid registration of cell nuclei in time-lapse microscopy images can be achieved through estimating the deformation fields using optical flow methods. In contrast to local optical flow models employed in the existing non-rigid registration methods, we introduce approaches based on a global optical flow model. Our registration model consists of a data fidelity term and a regularization term. We compared different regularizers for the deformation fields and found that a convex quadratic function is more suitable than non-convex ones. To improve the robustness, we propose an adaptive weighting scheme based on the statistics of the noise in fluorescence microscopy images as well as a combined local-global scheme. Moreover, we extend the global method by exploiting high-order image features. The best suitable high-order features are determined through learning two generative image models, namely, fields of experts and convolutional Gaussian restricted Boltzmann machine, whose model formulations are both consistent with the assumption of high-order feature constancy in the registration model. Using multiple data sets of real 2D and 3D live cell microscopy image sequences as well as synthetic image data, we demonstrate that our proposed approach outperforms the previous methods in terms of both registration accuracy and computational efficiency.
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7
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Arifulin EA, Sorokin DV, Tvorogova AV, Kurnaeva MA, Musinova YR, Zhironkina OA, Golyshev SA, Abramchuk SS, Vassetzky YS, Sheval EV. Heterochromatin restricts the mobility of nuclear bodies. Chromosoma 2018; 127:529-537. [PMID: 30291421 DOI: 10.1007/s00412-018-0683-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 09/25/2018] [Accepted: 09/26/2018] [Indexed: 12/24/2022]
Abstract
Nuclear bodies are relatively immobile organelles. Here, we investigated the mechanisms underlying their movement using experimentally induced interphase prenucleolar bodies (iPNBs). Most iPNBs demonstrated constrained diffusion, exhibiting infrequent fusions with other iPNBs and nucleoli. Fusion events were actin-independent and appeared to be the consequence of stochastic collisions between iPNBs. Most iPNBs were surrounded by condensed chromatin, while fusing iPNBs were usually found in a single heterochromatin-delimited compartment ("cage"). The experimentally induced over-condensation of chromatin significantly decreased the frequency of iPNB fusion. Thus, the data obtained indicate that the mobility of nuclear bodies is restricted by heterochromatin.
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Affiliation(s)
- Eugene A Arifulin
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Dmitry V Sorokin
- Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Anna V Tvorogova
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Margarita A Kurnaeva
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Yana R Musinova
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991, Moscow, Russia
- Koltzov Institute of Developmental Biology of Russian Academy of Sciences, Vavilov str. 26, 119334, Moscow, Russia
| | - Oxana A Zhironkina
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Sergey A Golyshev
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Sergey S Abramchuk
- Faculty of Chemistry, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Yegor S Vassetzky
- Koltzov Institute of Developmental Biology of Russian Academy of Sciences, Vavilov str. 26, 119334, Moscow, Russia.
- LIA 1066 LFR2O French-Russian Joint Cancer Research Laboratory, 94805, Villejuif, France.
- UMR8126, CNRS, Institut de Cancérologie Gustave Roussy, Université Paris-Sud, 94805, Villejuif, France.
| | - Eugene V Sheval
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991, Moscow, Russia.
- LIA 1066 LFR2O French-Russian Joint Cancer Research Laboratory, 94805, Villejuif, France.
- Department of Cell Biology and Histology, Faculty of Biology, Lomonosov Moscow State University, 119991, Moscow, Russia.
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8
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Arifulin EA, Musinova YR, Vassetzky YS, Sheval EV. Mobility of Nuclear Components and Genome Functioning. BIOCHEMISTRY (MOSCOW) 2018; 83:690-700. [PMID: 30195325 DOI: 10.1134/s0006297918060068] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cell nucleus is characterized by strong compartmentalization of structural components in its three-dimensional space. Certain genomic functions are accompanied by changes in the localization of chromatin loci and nuclear bodies. Here we review recent data on the mobility of nuclear components and the role of this mobility in genome functioning.
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Affiliation(s)
- E A Arifulin
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia.
| | - Y R Musinova
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia.,LIA 1066 LFR2O French-Russian Joint Cancer Research Laboratory, Villejuif, 94805, France.,Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow, 119334, Russia
| | - Y S Vassetzky
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia.,LIA 1066 LFR2O French-Russian Joint Cancer Research Laboratory, Villejuif, 94805, France.,Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow, 119334, Russia.,UMR8126, CNRS, Université Paris-Sud, Institut de Cancérologie Gustave Roussy, Villejuif, 94805, France
| | - E V Sheval
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia.,LIA 1066 LFR2O French-Russian Joint Cancer Research Laboratory, Villejuif, 94805, France
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