CTMC: Cell Tracking with Mitosis Detection Dataset Challenge

Anjum, Samreen; Gurari, Danna

doi:10.1109/cvprw50498.2020.00499

Cited by 19 publications

(13 citation statements)

References 21 publications

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“…Crowdworkers annotated a total of 2,468 frames, which is about 2.1% of the total number of frames, with an average of 14 key frames per object in a video 9 . Of these, 647 key frames were annotated for the NonIterative HITs, while 1,761 key frames were annotated for the Iterative HITs.…”

Section: Results -Human Effortmentioning

confidence: 99%

“…We also selected 10 biomedical videos showing migrating cells from the CTMC dataset [9], which supports the biomedical community. The total number of cells in these videos vary from 5 to 11 cells, which includes children cells that appear from a parent cell repeatedly undergoing mitosis (cell division).…”

Section: Datasets and Ground Truthmentioning

confidence: 99%

“…We conducted this study on a larger collection of 116,394 frames that came from 66 live cell videos from the CTMC Cell Motility dataset [9]. The average number of frames per video is 1,764.…”

Section: Datasetmentioning

confidence: 99%

“…These videos came from the MOT dataset: https://motchallenge.net/ 2 These videos came from the CTMC dataset[9]. We collected ground truth data for all videos from two in-house experts 3 who we trained to perform video annotation.4 Of note, we conducted this experiment before June 2019, and since then, the AMT API used by the VATIC system has been deprecated, rendering VATIC incompatible for crowdsourcing with AMT.…”

mentioning

confidence: 99%

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CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos

Anjum,

Lin,

Gurari

2020

Preprint

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Crowdsourcing is a valuable approach for tracking objects in videos in a more scalable manner than possible with domain experts. However, existing frameworks do not produce high quality results with non-expert crowdworkers, especially for scenarios where objects split. To address this shortcoming, we introduce a crowdsourcing platform called CrowdMOT, and investigate two micro-task design decisions: (1) whether to decompose the task so that each worker is in charge of annotating all objects in a sub-segment of the video versus annotating a single object across the entire video, and (2) whether to show annotations from previous workers to the next individuals working on the task. We conduct experiments on a diversity of videos which show both familiar objects (aka -people) and unfamiliar objects (aka -cells). Our results highlight strategies for efficiently collecting higher quality annotations than observed when using strategies employed by today's state-of-art crowdsourcing system.

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Section: Results -Human Effortmentioning

confidence: 99%

Section: Datasets and Ground Truthmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

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CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos

Anjum,

Lin,

Gurari

2020

Preprint

Self Cite

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“…Specifically, to obtain the training data, the exact outline of hundreds of cells must be carefully drawn in multiple images ( Supplementary Figure S1, Additional File S1 ), which is time consuming and often challenging for users [ 34 , 35 ]. Alternatively, AI-based methods that do not rely on stringent object segmentation but instead use rectangular bounding boxes to enclose objects for detection [ 36 ] require training data that are easier and faster to generate [ 34 , 35 ]. Specifically, obtaining the training data only requires two clicks of a mouse to specify the upper-leftmost and lower-rightmost corners of a bounding box for each cell in the dataset ( Supplementary Figure S1, Additional File S1 ).…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Cell Tracking to Enable High-Throughput Drug Screening Targeting Airway Epithelial Repair for Children with Asthma

Gwatimba

Rosenow

Stick

et al. 2022

JPM

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The airway epithelium of children with asthma is characterized by aberrant repair that may be therapeutically modifiable. The development of epithelial-targeting therapeutics that enhance airway repair could provide a novel treatment avenue for childhood asthma. Drug discovery efforts utilizing high-throughput live cell imaging of patient-derived airway epithelial culture-based wound repair assays can be used to identify compounds that modulate airway repair in childhood asthma. Manual cell tracking has been used to determine cell trajectories and wound closure rates, but is time consuming, subject to bias, and infeasible for high-throughput experiments. We therefore developed software, EPIC, that automatically tracks low-resolution low-framerate cells using artificial intelligence, analyzes high-throughput drug screening experiments and produces multiple wound repair metrics and publication-ready figures. Additionally, unlike available cell trackers that perform cell segmentation, EPIC tracks cells using bounding boxes and thus has simpler and faster training data generation requirements for researchers working with other cell types. EPIC outperformed publicly available software in our wound repair datasets by achieving human-level cell tracking accuracy in a fraction of the time. We also showed that EPIC is not limited to airway epithelial repair for children with asthma but can be applied in other cellular contexts by outperforming the same software in the Cell Tracking with Mitosis Detection Challenge (CTMC) dataset. The CTMC is the only established cell tracking benchmark dataset that is designed for cell trackers utilizing bounding boxes. We expect our open-source and easy-to-use software to enable high-throughput drug screening targeting airway epithelial repair for children with asthma.

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