Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 28 tok/s
Gemini 2.5 Pro 40 tok/s Pro
GPT-5 Medium 16 tok/s Pro
GPT-5 High 13 tok/s Pro
GPT-4o 103 tok/s Pro
Kimi K2 197 tok/s Pro
GPT OSS 120B 471 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection (2403.13412v1)

Published 20 Mar 2024 in cs.CV

Abstract: 3D cell tracking in a living organism has a crucial role in live cell image analysis. Cell tracking in C. elegans has two difficulties. First, cell migration in a consecutive frame is large since they move their head during scanning. Second, cell detection is often inconsistent in consecutive frames due to touching cells and low-contrast images, and these inconsistent detections affect the tracking performance worse. In this paper, we propose a cell tracking method to address these issues, which has two main contributions. First, we introduce cell position heatmap-based non-rigid alignment with test-time fine-tuning, which can warp the detected points to near the positions at the next frame. Second, we propose a pairwise detection method, which uses the information of detection results at the previous frame for detecting cells at the current frame. The experimental results demonstrate the effectiveness of each module, and the proposed method achieved the best performance in comparison.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (16)
  1. “Significantly improved precision of cell migration analysis in time-lapse video microscopy through use of a fully automated tracking system,” BMC cell biology, vol. 11, pp. 1–12, 2010.
  2. “Cell image analysis: Algorithms, system and applications,” in WACV. IEEE, 2011.
  3. “Cell tracking under high confluency conditions by candidate cell region detection-based-association approach,” in Proceedings of Biomedical Engineering, 2011, pp. 1004–1010.
  4. “Joint multi-frame detection and segmentation for multi-cell tracking,” ICIG, 2019.
  5. “Hierarchical partial matching and segmentation of interacting cells,” in MICCAI, 2012, pp. 389–396.
  6. “Reliable cell tracking by global data association,” in ISBI. IEEE, 2011, pp. 1004–1010.
  7. “A benchmark for epithelial cell tracking,” in ECCVW, 2018.
  8. “Cell tracking with deep learning for cell detection and motion estimation in low-frame-rate,” in MICCAI, 2019, pp. 397–405.
  9. “MPM: Joint representation of motion and position map for cell tracking,” in CVPR, 2020.
  10. “A semi-automatic cell tracking process towards completing the 4d atlas of c. elegans development,” arXiv preprint arXiv:2207.13611, 2022.
  11. “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI. Springer, 2015, pp. 234–241.
  12. “C. elegans cell matching and tracking in a 4d imageing system,” in ICASSP. IEEE, 2015, pp. 937–941.
  13. “Voxelmorph: a learning framework for deformable medical image registration,” IEEE TMI, vol. 38, no. 8, pp. 1788–1800, 2019.
  14. “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3DV. IEEE, 2016, pp. 565–571.
  15. “Weakly supervised cell instance segmentation by propagating from detection response,” in MICCAI. Springer, 2019, pp. 649–657.
  16. “Ilastik: interactive machine learning for (bio) image analysis,” Nature methods, vol. 16, no. 12, pp. 1226–1232, 2019.
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.