Crowdsourced generation of annotated video datasets: A zebrafish larvae dataset for video segmentation and tracking evaluation
- Publication Type:
- Conference Proceeding
- Citation:
- 2017 IEEE Life Sciences Conference, LSC 2017, 2018, 2018-January pp. 274 - 277
- Issue Date:
- 2018-01-23
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 08268196.pdf | Published version | 1.3 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2017 IEEE. Video segmentation research has emerged over the last decade for biomedical image and video processing, especially in biological organism tracking. However, due to the difficulties in generating the video segmentation ground truth, the general lack of segmentation datasets with annotated ground-truth severely limits the evaluation of segmentation algorithms. This paper proposes an efficient and scalable crowdsourced approach to generate video segmentation ground-Truth to facilitate database generation for general biological organism segmentation and tracking algorithm evaluation. To illustrate the proposed approach, an annotated zebrafish larvae video segmentation dataset has been generated and made freely available online. To enable the evaluation of algorithms against a ground-Truth, a set of segmentation evaluation metrics are also presented. The segmentation performance of five leading segmentation algorithms is then evaluated by the metrics on the generated zebrafish video segmentation dataset.
Please use this identifier to cite or link to this item: