3D CT Scan Segmentation With Occupancy Network

This repo implements a 3D superresolution segmentation task for an airport baggage dataset.

Our final paper can be found here.

Model Architecture

To solve the problem of binary classification an Occupancy Network is utilize. The occupancy network is implemented with and without concatenation of the latent tensor from the encoding to the decoding path. Additionally conditional batch normalization in the decoding path can be utilized.

Original occupancy network with conditional batch normalization and without concatenation Occupancy network without conditional batch normalization and with concatenation

Usage

The occupancy network can be trained and/or tested, on the airport baggage dataset, by simply executing the main file.

python main.py

The following arguments can be passed to the main.py script.

Argument	Default value	Info
`--train`	1 (True)	Flag to perform training
`--test`	1 (True)	Flag to perform testing
`--batch_size`	10	Batch size to be utilized
`--lr`	1e-04	Learning rate to use
`--gpus_to_use`	'0'	Indexes of the GPUs to be use
`--use_data_parallel`	0 (False)	Use multiple GPUs (num of GPUs must be a factor of the batch size)
`--epochs`	100	Epochs to perform while training
`--use_cat`	1 (True)	One if concatenation should be utilized
`--use_cbn`	1 (True)	One if conditional BN should be utilized else normal BN is used
`--loss`	'cross_entropy'	Loss function to be utilized ('cross_entropy', 'dice' or 'focal')
`--load_model`	'None'	Path to model to be loaded

This repo implements a 3D segmentation task for an airport baggage dataset.

Related tags

Overview

3D CT Scan Segmentation With Occupancy Network

Model Architecture

Usage

Results

Owner

Christoph Reich

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