3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

By Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang. IEEE Transactions on Medical Imaging, 2018

392 citations recorded by the source corpus.

Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.

Subfields: Biomedical Engineering, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/001b107a1d36f504119575f60cc103734efcdd45

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In Biomedical Engineering

In Pulmonary and Respiratory Medicine

Ge Wang

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018, background): “However, it has been pointed out in [68], [69] that high PSNR and SSIM values cannot guarantee a visually favorable result.”
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, methodology): “This transfer learning process is adopted in almost every modality of medical imaging, including X-rays, CT scans, pathological images, positron emission tomography (PET), and MRI [85].”
Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation (2019, neutral): “ficient training data [28], [44], [45].”
Low-dose CT image and projection dataset (2020, methodology): “A residual encoder–decoder convolutional neural network (RED-CNN) for 2D and 3D CT denoising.(26,27)”