Paper | Installation | Basic Usage | Inference Pipelines
Comp2Comp is a library for extracting clinical insights from computed tomography scans.
# Install from local clone:
git clone https://github.com/StanfordMIMI/Comp2Comp/
# Install script requires Anaconda/Miniconda.
cd Comp2Comp && bin/install.sh
For installing on the Apple M1 chip, see these instructions.
bin/C2C <pipeline_name> --input_path <path/to/input/folder>
For running on slurm, modify the above commands as follow:
bin/C2C-slurm <pipeline_name> --input_path <path/to/input/folder>
We have designed Comp2Comp to be highly extensible and to enable the development of complex clinically-relevant applications. We observed that many clinical applications require chaining several machine learning or other computational modules together to generate complex insights. The inference pipeline system is designed to make this easy. Furthermore, we seek to make the code readable and modular, so that the community can easily contribute to the project.
The InferencePipeline
class is used to create inference pipelines, which are made up of a sequence of InferenceClass
objects. When the InferencePipeline
object is called, it sequentially calls the InferenceClasses
that were provided to the constructor.
The first argument of the __call__
function of InferenceClass
must be the InferencePipeline
object. This allows each InferenceClass
object to access or set attributes of the InferencePipeline
object that can be accessed by the subsequent InferenceClass
objects in the pipeline. Each InferenceClass
object should return a dictionary where the keys of the dictionary should match the keyword arguments of the subsequent InferenceClass's
__call__
function. If an InferenceClass
object only sets attributes of the InferencePipeline
object but does not return any value, an empty dictionary can be returned.
Below are the inference pipelines currently supported by Comp2Comp.
bin/C2C spine --input_path <path/to/input/folder>
- input_path should contain a DICOM series or subfolders that contain DICOM series.
bin/C2C muscle_adipose_tissue --input_path <path/to/input/folder>
- DICOM files within the input_path folder and subfolders of input_path will be processed.
bin/C2C spine_muscle_adipose_tissue --input_path <path/to/input/folder>
- input_path should contain a DICOM series or subfolders that contain DICOM series.
bin/C2C contrast_phase --input_path <path/to/input/folder>
- input_path should contain a DICOM series or subfolders that contain DICOM series.
bin/C2C liver_spleen_pancreas --input_path <path/to/input/folder>
- input_path should contain a DICOM series or subfolders that contain DICOM series.
- Abdominal Aortic Aneurysm Detection
- Hip Analysis
@article{blankemeier2023comp2comp,
title={Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography},
author={Blankemeier, Louis and Desai, Arjun and Chaves, Juan Manuel Zambrano and Wentland, Andrew and Yao, Sally and Reis, Eduardo and Jensen, Malte and Bahl, Bhanushree and Arora, Khushboo and Patel, Bhavik N and others},
journal={arXiv preprint arXiv:2302.06568},
year={2023}
}
In addition to Comp2Comp, please consider citing TotalSegmentator:
@article{wasserthal2022totalsegmentator,
title={TotalSegmentator: robust segmentation of 104 anatomical structures in CT images},
author={Wasserthal, Jakob and Meyer, Manfred and Breit, Hanns-Christian and Cyriac, Joshy and Yang, Shan and Segeroth, Martin},
journal={arXiv preprint arXiv:2208.05868},
year={2022}
}