Here's the link to the original DeepSEED repository.
https://github.com/ymli39/DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection

There were a few mistakes in the original DeepSEED code, but they've been taken care of in this repository.

1. The paper results were not reproduced in the original DeepSEED code, but a bug fix made it possible.
2. Fixed an issue with memory during the validation process.
3. Corrected the way arguments were functioning.

DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection

DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder ConvNets for Pulmonary Nodule Detection

Dataset: LUNA16 can be downloaded from https://luna16.grand-challenge.org/data/

LIDC-IDR can be downloaded from https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI

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Preprocessing: Go to config_training.py, create two directory Luna_data and Preprocess_result_path. Then change directory listed as follows:

Luna_raw: raw data folder downloaded from LUNA16 website

Luna_segment: luna segmentation download from LUNA16 website

Luna_data: temporary folder to store luna data

Preprocess_result_path: final preprocessed data folder

Run prepare.py, output LUNA16 data can be found inside folder Preprocess_result_path, with saved images as _clean.npy, _label.npy for training, and _spacing.npy, _extendbox.npy, _origin.npy are separate information for data testing.

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Training: Go to ./detector directory, the model can be trained by calling the following script:

CUDA_VISIBLE_DEVICES=0,1,2,3,4 python train_detector_se.py -b 16 --save-dir /train_result/ --epochs 150

The output model can be found inside ./train_result/ folder.

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Testing: In order to obtain the predicted label, go to ./detector directory, the model can be tested by calling the following script:

CUDA_VISIBLE_DEVICES=0,1,2,3,4 python train_detector_se.py -b 1 --resume ‘best_model.ckpt’ --test 1 --save-dir /output/

The bbox output can be found inside ./output/bbox/, the predicted bounding boxes and ground truth bounding boxes will be saved in this direction.

Then for FROC metric evaluation, go to FROCeval.py, change path for following directories:

seriesuids_filename: patient ID for testing

detp: threshold for bounding boxes regression

nmsthresh: threshold used bounding boxes non-maximum suppression

bboxpath: directory which stores bounding boxes from testing output

Frocpath: path to store FROC metrics

Outputdir: store the metric evaluation output

The FROC evaluation script is provided from LUNA16 website, you can find the script in noduleCADEvaluationLUNA16.py.

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You could refer to the arxiv paper for more details and performance:

@inproceedings{li2020deepseed,
  title={DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection},
  author={Li, Yuemeng and Fan, Yang},
  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
  pages={1866--1869},
  year={2020},
  organization={IEEE}
}