| 1. |
- Maska, Martin, et al.
(författare)
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A benchmark for comparison of cell tracking algorithms
- 2014
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 30:11, s. 1609-1617
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.
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| 2. |
- Shahzad, Rahil, et al.
(författare)
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Automated extraction and labelling of the arterial tree from whole-body MRA data
- 2015
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 24:1, s. 28-40
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we present a fully automated algorithm for extraction of the 3D arterial tree and labelling the tree segments from whole-body magnetic resonance angiography (WB-MRA) sequences. The algorithm developed consists of two core parts (i) 3D volume reconstruction from different stations with simultaneous correction of different types of intensity inhomogeneity, and (ii) Extraction of the arterial tree and subsequent labelling of the pruned extracted tree. Extraction of the arterial tree is performed using the probability map of the "contrast" class, which is obtained as one of the results of the inhomogeneity correction scheme. We demonstrate that such approach is more robust than using the difference between the pre- and post-contrast channels traditionally used for this purpose. Labelling the extracted tree is performed by using a combination of graph-based and atlas-based approaches. Validation of our method with respect to the extracted tree was performed on the arterial tree subdivided into 32 segments, 82.4% of which were completely detected, 11.7% partially detected, and 5.9% were missed on a cohort of 35 subjects. With respect to automated labelling accuracy of the 32 segments, various registration strategies were investigated on a training set consisting of 10 scans. Further analysis on the test set consisting of 25 data sets indicates that 69% of the vessel centerline tree in the head and neck region, 80% in the thorax and abdomen region, and 84% in the legs was accurately labelled to the correct vessel segment. These results indicate clinical potential of our approach in enabling fully automated and accurate analysis of the entire arterial tree. This is the first study that not only automatically extracts the WB-MRA arterial tree, but also labels the vessel tree segments.
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| 3. |
- Zhang, Yun, et al.
(författare)
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Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain
- 2023
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Ingår i: Scientific Reports. - 2045-2322. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer (https://viewer.cytosplore.org) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.
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