Thyroid Nodule Segmentation in Ultrasound Image Based on Information Fusion of Suggestion and Enhancement Networks
Introduction
This reposity is to implement our proposed network for enhancing the performance of thyroid nodule segmentation problem of conventional UNet-based network.
Please note that, the pretrained models were obtained using our network (NANets) on either TDID [1] or 3DThyroid [2] datasets.
[1] Pedraza, L.; Vargas, C.; Narvaez, F.; Duran, O.; Munoz, E.; Romero, E. (2015). An open access thyroid ultrasound-image database. In Proceedings of the 10th International Symposium on Medical Information Processing and Analysis, Cartagena de Indias, Colombia, 28 January 2015; Volume 9287, pp. 1–6.
[2] Wunderling, T.; Golla, B.; Poudel, P.; Arens, C.; Friebe, M.; Hansen, C. (2017). Comparison of thyroid segmentation techniques for 3D ultrasound. Proceedings of SPIE Medical Imaging, Orlando, USA, 2017; https://doi.org/10.1117/12.2254234
Usage Instruction
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To train our proposed network with a custom dataset, please use the main.py script and set the train_flag flag to True.
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To perform inference using our provided pretrained network, please provide the test dataset (itest_db in the main.py), and set the train_flag to False.
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Providing the trainining, testing, and validation set by customizing the bellow part in main.py.
itrain_db, ival_db, itest_db = None, None, None
In which:
- itrain_db, ival_db and itest_db are the training, validation, and testing datasets.
- itrain_db, ival_db and itest_db are the lists of (2d_image, 2d_mask) pairs
- 2d_image is (0,255) gray or color image
- 2d_mask is (0,1) label image.
Requiremetns
- Python >= 3.5
- Tensorflow >= 2.1.0
- Window 10
Any work that uses the code and provided pretrained network must acknowledge the authors by including the following reference.
Dat Tien Nguyen, Jiho Choi, and Kang Ryoung Park, “Thyroid Nodule Segmentation in Ultrasound Image Based on Information Fusion of Suggestion and Enhancement Networks,” Mathematics, in submission.
Example Results
