This release is a pre-release and may not be stable for production use.
JCELL
jcell is an image segmentation software primarily dedicated to biological cells. The software is an implementation of our latest developments in cell segmentation using deep learning approaches specially aimed at segmenting clustered cells. We pioneered the use of multiclass segmentation to improve segmenting tightly packed cells (see ICIP2018) and recently introduced a combination of Youden's J Statistics and cross entropy as a robust loss working in tandem to promote a sharp classification of pixels/voxels (see ISBI2020). Due to J, highly imbalanced data can be directly trained bypassing the need of complicated data balancing strategies. This is the software used for winning DIC-C2DH-Hela and PhC-C2DH-U373 Cell segmentation challenges (http://celltrackingchallenge.net/latest-csb-results/).
For an in depth understading how the methods work check our publications [ICIP2018], [MICCAIW2019], [ISBI2020].
See documentation for more details on how to use this software.
[ISBI2020] J Regularization Improves Imbalanced Multiclass Segmentation. Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Paul T. Tarr, Tsang Ing Ren, Elliot M. Meyerowitz, Alexandre Cunha. IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020. https://ieeexplore.ieee.org/abstract/document/9098550. Arxiv https://arxiv.org/abs/1910.09783.
[MICCAIW2019] A Weakly Supervised Method for Instance Segmentation of Biological Cells. Fidel A. Guerrero Pena, Pedro D. Marrero Fernandez, Tsang Ing Ren, Alexandre Cunha. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. Springer. 2019. https://link.springer.com/chapter/10.1007/978-3-030-33391-1_25. Arxiv https://arxiv.org/abs/1908.09891.
[ICIP2018] Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells. Fidel A. Guerrero Pena, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha. IEEE International Conference on Image Processing (ICIP). 2018. https://ieeexplore.ieee.org/abstract/document/8451187/. Arxiv https://arxiv.org/abs/1802.07465.
1 maintainer
Metadata
1 maintainer
Release files for jcell-ISBI 0.0.1a4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| jcell_ISBI-0.0.1a4-py3-none-any.whl | Python 3 | none | any | Details for jcell_ISBI-0.0.1a4-py3-none-any.whl |
Release files / jcell_ISBI-0.0.1a4-py3-none-any.whl
| Download URL | jcell_ISBI-0.0.1a4-py3-none-any.whl |
|---|---|
| Size | 91.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d767922a472aedec7c181bb20f4206167f42711be9be87c9585ec8dbec2ccbca
|
|
BLAKE2b-256 checksum How to use checksums |
7f5f9b2e99d1a17b60099813ac7b81a42c023efa3812a75f7baf6509f19f565b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.1 importlib_metadata/3.7.3 pkginfo/1.5.0.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.7.4
|
Security for jcell-ISBI 0.0.1a4
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
