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Cited by 1,826Cell Image Analysis TechniquesAdvanced Neural Network ApplicationsDigital Imaging for Blood Diseases

Cell Detection with Star-Convex Polygons

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Schmidt, Uwe, Weigert, Martin, Broaddus, Coleman, Myers, Gene·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-00934-2_30
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  • The LiveCell study used a different average precision score from ours, which additionally requires a confidence score per ROI, while we used the average precision formulation from the Data Science Bowl challenge and other studies.

    “Finally, we point out that the LiveCell study used a different average precision score from ours, which additionally requires a confidence score per ROI, while we used the average precision formulation from the Data Science Bowl challenge and other studies12,27,35.”

    From Cellpose 2.0: how to train your own model (2022) · cites this paper as [35]

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Research works printed but not linked: 17

  1. [1]1. Amat, F., Lemon, W., Mossing, D.P., McDole, K., Wan, Y., Branson, K., Myers, E.W., Keller, P.J.: Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nature methods 11 (9), 951 (2014)
  2. [2]2. Boutros, M., Heigwer, F., Laufer, C.: Microscopy-based high-content screening. Cell 163 (6), 1314-1325 (2015)
  3. [3]3. Caicedo, J.C., Roth, J., Goodman, A., Becker, T., Karhohs, K.W., McQuin, C., Singh, S., Theis, F., Carpenter, A.E.: Evaluation of deep learning strategies for nucleus segmentation in fluorescence images. bioRxiv (2018)
  4. [4]4. Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: Deep contour-aware networks for accurate gland segmentation. In: CVPR (2016)
  5. [5]5. Guerrero-Pena, F.A., Marrero Fernandez, P.D., Ren, T.I., Yui, M., Rothenberg, E., Cunha, A.: Multiclass weighted loss for instance segmentation of cluttered cells. arXiv (2018)
  6. [6]6. He, K., Gkioxari, G., Doll´ ar, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)
  7. [7]7. Jetley, S., Sapienza, M., Golodetz, S., Torr, P.H.: Straight to shapes: Real-time detection of encoded shapes. In: CVPR (2017)
  8. [8]8. Jug, F., Levinkov, E., Blasse, C., Myers, E.W., Andres, B.: Moral lineage tracing. In: CVPR (2016)
  9. [9]9. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single shot multibox detector. In: ECCV (2016)
  10. [10]10. Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zheng, Y., Xue, X.: Arbitraryoriented scene text detection via rotation proposals. IEEE Transactions on Multimedia (2018)
  11. [11]11. Meijering, E.: Cell segmentation: 50 years down the road. IEEE Signal Processing Magazine 29 (5), 140-145 (2012)
  12. [13]13. Rempfler, M., Kumar, S., Stierle, V., Paulitschke, P., Andres, B., Menze, B.H.: Cell lineage tracing in lens-free microscopy videos. In: MICCAI (2017)
  13. [14]14. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS (2015)
  14. [16]16. Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.A.: Ilastik: Interactive learning and segmentation toolkit. In: Int. Symposium on Biomedical Imaging (2011)
  15. [17]17. Ulman, V., Maˇ ska, M., Magnusson, K.E., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., et al.: An objective comparison of cell-tracking algorithms. Nature methods 14 (12), 1141 (2017)
  16. [19]19. Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Computer methods in biomechanics and biomedical engineering: Imaging & Visualization 6 (3), 283-292 (2018)
  17. [20]20. Yurchenko, V., Lempitsky, V.: Parsing images of overlapping organisms with deep singling-out networks. In: CVPR (2017)

Cited by

Sorted by citations

Authors
Schmidt, U., Weigert, M., Broaddus, C., & Myers, G.
Affiliations
Center for Systems Biology Dresden (DE)
Corresponding author
  • Schmidt, Uwe
  • Weigert, Martin
ORCID
Weigert, Martin
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
265-273
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-00934-2_30
OpenAlex
W2806530968
Cited by
1,826
References
2
Citations by year
2026: 340 · 2025: 443 · 2024: 408 · 2023: 306 · 2022: 165 · 2021: 110 · 2020: 44 · 2019: 9 · 2018: 1
FWCI
114.54
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
Keywords
cell detection, cell segmentation, shape representation, fluorescence microscopy images, convolutional neural networks

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