Metadata-Version: 2.1
Name: cell-AAP
Version: 0.0.1
Author: Anish Virdi
Classifier: Framework :: napari
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: napari[all] >=0.4.19
Requires-Dist: numpy >=1.26.4
Requires-Dist: opencv-python >=4.9.0
Requires-Dist: tifffile >=2024.2.12
Requires-Dist: torch >=2.3.0
Requires-Dist: torchvision >=0.18.0
Requires-Dist: scikit-image >=0.22.0
Requires-Dist: qtpy >=2.4.1
Requires-Dist: pillow >=10.3.0
Requires-Dist: scipy >=1.3.0

# cellular-Automated Annotation Pipeline
Utilities for the semi-automated generation of instance segmentation annotations to be used for neural network training. Utilities are built ontop of [UMAP](https://github.com/lmcinnes/umap), [HDBSCAN](https://arxiv.org/abs/1911.02282) and a finetuned encoder version of FAIR's [Segment Anything Model](https://github.com/facebookresearch/segment-anything/tree/main?tab=readme-ov-file) developed by Computational Cell Analytics for the project [micro-sam](https://github.com/computational-cell-analytics/micro-sam/tree/master/micro_sam/sam_annotator). In addition to providing utilies for annotation building, we train a network, FAIR's [detectron2](https://github.com/facebookresearch/detectron2) to 
1. Demonstrate the efficacy of our utilities. 
2. Be used for microscopy annotation of supported cell lines 

Supported cell lines currently include:
1. HeLa

In development cell lines currently include:
1. U2OS
2. HT1080

We've developed a napari application for the usage of this pre-trained network and propose a transfer learning schematic for the handling of new cell lines. 




