3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (1802.05656v2)

Abstract: Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction of CT radiation dose, however, compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in low-dose CT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN). This article introduces a Contracting Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D configurations within the GAN framework for low-dose CT denoising. A novel feature of our approach is that an initial 3D CPCE denoising model can be directly obtained by extending a trained 2D CNN and then fine-tuned to incorporate 3D spatial information from adjacent slices. Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch. By comparing the CPCE with recently published methods based on the simulated Mayo dataset and the real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better performance, suppressing image noise and preserving subtle structures.

• The paper introduces a novel 3D convolutional encoder-decoder network that leverages transfer learning from a pre-trained 2D model to improve low-dose CT denoising.
• Methodologically, the network integrates a feature-extracting encoder with a decoder that reconstructs high-quality volumetric CT images, mitigating the need for expansive 3D datasets.
• Results indicate significant gains in PSNR and SSIM, highlighting cross-dimensional transfer learning’s potential to lower radiation dosage without compromising image clarity.

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

The paper entitled "3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network" presents a novel approach to addressing the challenge of denoising low-dose computed tomography (CT) scans, which is a significant concern in medical imaging due to the necessity of reducing radiation exposure for patients. The authors propose a 3D convolutional encoder-decoder network that leverages transfer learning from a pre-trained 2D model, aiming to enhance the denoising performance over existing methodologies.

Methodology and Approach

The central contribution of the paper is the integration of a 3D convolutional neural network (CNN) framework with transfer learning, capitalizing on the rich representational capabilities of networks trained on 2D image datasets. The authors first train a 2D network using a large corpus of two-dimensional CT slice datasets. The learned parameters from this network are then used to initialize a 3D network, which is subsequently fine-tuned using three-dimensional CT data. This approach mitigates the requirement for extensive 3D training datasets, which are often more challenging to acquire.

The network architecture employed is a convolutional encoder-decoder model that adapts well to the volumetric nature of CT data. The encoder is responsible for extracting hierarchical features from the input data, while the decoder reconstructs the denoised output from these features. The transition from 2D to 3D is handled via convolutional blocks that respect the depth dimension of the input data.

Results

The authors demonstrate that the proposed method outperforms traditional denoising techniques and prior models that rely solely on 2D or naïve 3D approaches. Through quantitative analysis, the paper reports significant improvements in metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to baseline methods. Specifically, the transfer learning component leads to a noticeable enhancement in the generalization ability and efficiency of the 3D model, a result supported by the strong numerical outcomes presented.

Implications and Future Directions

This paper represents an important advancement in the domain of medical image processing, particularly within the context of low-dose CT imaging. The successful application of transfer learning from 2D to 3D networks opens avenues for similar strategies in other volumetric data domains. From a theoretical standpoint, the research suggests that cross-dimensional transfer learning can be highly effective, encouraging further exploration into hybrid models that blend 2D and 3D network features.

Practically speaking, this approach has the potential to significantly improve patient outcomes by enabling the use of lower radiation doses without compromising image quality. Future research may focus on enhancing the scalability of the method, exploring applications beyond CT imaging, and refining the model architectures to further reduce computational complexity while maintaining or improving denoising performance. Additionally, understanding the transferability of features across dimensions might yield insights applicable to broader applications in computer vision and machine learning.