LIDC-IDRI-Nodule Analysis Code

Personal toolbox for lidc-idri dataset / lung cancer / nodule
This project is a personal toolbox, but it can really help to get information from LIDC-IDRI.
I am willing to make it better with your help.

Code in codeForLIDC is used for LIDC-IDRI researches. For the label information, you can refer to Shen S , Han S X , Aberle D R , et al. An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification[J]. 2018..

Key code in CodeForLIDC

get_PatientId_SeriesUID.py

It gives out the id_scan.txt, which can help to to load CT files according to patient Id.

get_scan_xml.py

It gets nodule's id and it's nine features from the xml files and gives out nodule_chara_list.csv.

orignize_list_nodule_chara.py

It gives out the malignancy.csv, which combine the nodule_chara_list.csv and the list3.2.csv. With malignancy.csv, you can get all information about labeled nodules.

get_dicom_info.py

It can get nodules' images and labels with malignancy.csv and id_scan.txt. By running this code, you can have nodule images and their masks (single mask).

If you only want to get the data, you can run the code:

noduleinfo = csvTools.readCSV('files/malignancy.csv')
idscaninfo = csvTools.readCSV('files/id_scan.txt')

Then use pydicom to process the DICOM files. I save the relationships between case id and dicom path in the 'malignancy.csv', and 'id_scan.txt', which can be found in 'LIDC-IDRI-Toolbox-python/codeForLIDC/files/'.

CodeForMulitpleAnnotations

Get nodules with multiple segmentation annotations. The method has been used in Wang, Qiuli, et al. "Uncertainty-Aware Lung Nodule Segmentation with Multiple Annotations." arXiv preprint arXiv:2110.12372 (2021). You can get the processed data here: https://pan.baidu.com/s/1nQLaS_NEaiBeGOeHxHV1dg, Code: 4f38

xmlopt.py

It can extract label edges from xml files.

get_data.py

By running python get_data.py in CodeForMulitpleAnnotations, you can have nodule images and their multiple annotations' intersection, union, and difference.

If you find it helpful to your research, please cite as follows:

@inproceedings
{yang2022uncertainty,
 title={Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention},
 author={Yang, Han and Shen, Lu and Zhang, Mengke and Wang, Qiuli},
 booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2022: 25th International Conference, Singapore, September 18--22, 2022,    Proceedings, Part V},
 pages={44--54},
 year={2022},
 organization={Springer}
}

@inproceedings{wang2020class,
  title={Class-aware multi-window adversarial lung nodule synthesis conditioned on semantic features},
  author={Wang, Qiuli and Zhang, Xingpeng and Chen, Wei and Wang, Kun and Zhang, Xiaohong},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part VI 23},
  pages={589--598},
  year={2020},
  organization={Springer}
}

Feel free to contact me (qiuli.wang0102@foxmail.com) if you have any problems.