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Cell-Segmentation

Official Implementation of Auto-Segmentation and Time-Dependent Systematic Analysis of Mesoscale Cellular Structure in β-Cells During Insulin Secretion.

Description of folders

dataloaders: scripts for preprocessing input data, data augmentation.
experiments: scripts for evaluating semantic results: cell/nucleus/mitochondria.
figure: the workflow figure for this paper.
networks: framework structure of unet.
script: commands to run model on test data.
utils: function for calculating loss, ramps.

Cell IDs

Partition of training/validation/testing: 18/3/3

Training set: 766_10, 766_11, 766_2, 766_7, 769_5, 769_7, 783_12 783_6, 784_4, 784_6, 784_7, 785_7, 822_4, 822_6, 822_7, 842_13, 931_14, 931_9,
Validation set: 766_5, 783_5, 842_12,
Testing set: 766_8, 784_5, 842_17,

Installation

Dependencies

  • Python 3.7.3
  • PyTorch 1.3.1
  • torchvision 0.4.2
  • Cuda version 10.0

alt text

Get Started

git clone https://github.com/Xiangyi1996/Cell-Segmentation.git
cd Cell-Segmentation
mkdir logs
mkdir data
mkdir results

Data Preparation for Soft X-ray tomograms (SXT) Dataset

1. Download SXT dataset from PBC

Please go to PBC Dataset and download SXT images and manual labels, and put them under data folder.

# symlink the pascal dataset
cd data
ln -s /path_to_sxt_images/ ./image_xyz
ln -s /path_to_manual_labels/ ./mask_xyz

2. Download pretrained model Download the FS_mito and FS_mem_nu weights and put them under ./results folder.

# download the pretrained model
cd results
mkdir FS_mito
mkdir FS_mem_nu

Inference in Command Line for test data

# Test mitochondria model 
sh script/test_mito.sh

# Test membrane and nuclear model
sh script/test_mem_nu.sh

There are some hyperparameters that you can adjust in the script.

python test/eval_mito.py --gpu 0 --exp FS_mito --num-workers 8 --batch-size 1 --num-classes 4 --test_idx 'iso'

PS: You can change the EXP in script to your pretrained model name. The above is just an example.

Results

We list the performance w/o 3D fusion.

Before 3D fusion Membrane Nucleus Mito
766_8 90.74 93.21 68.58
784_5 87.43 89.95 63.17
842_17 85.34 83.50 65.03
mean 87.84 88.89 65.59
After 3D fusion Membrane Nucleus Mito
766_8 93.54 93.92 70.34
784_5 89.41 91.82 67.29
842_17 91.85 89.49 67.40
mean 91.60 91.74 68.34

RDF Results

And the RDF of our results can be downloaded here