Correction for “3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2D Trained Network” [Jun 18 1522-1534]

Shan, Hongming; Zhang, Yi; Yang, Qingsong; Krüger, Uwe; Kalra, Mannudeep K.; Sun, Ling; Cong, Wenxiang; Wang, Ge

doi:10.1109/tmi.2018.2878429

Cited by 24 publications

(37 citation statements)

References 32 publications

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“…The results demonstrate that the G-Forward achieves the highest scores using the evaluation metrics, PSNR and SSIM, which outperforms all other methods. However, it has been pointed out in [81], [82] that high PSNR and SSIM values cannot guarantee a visually favorable result. Non-GAN based methods (FSRCNN, ESPCN, LapSRN) may fail to recover some fine structure for diagnostic evaluation, such as shown by zoomed boxes in Fig.…”

Section: Experimental Results With the Tibia Datasetmentioning

confidence: 99%

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

You¹,

Li²,

Zhang³

et al. 2020

IEEE Trans. Med. Imaging

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356

270

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Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from lowresolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this deep imaging process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel 1 × 1 1 × 1 1 × 1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for superresolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three largescale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

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Section: Experimental Results With the Tibia Datasetmentioning

confidence: 99%

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

You¹,

Li²,

Zhang³

et al. 2020

IEEE Trans. Med. Imaging

Self Cite

356

270

View full text Add to dashboard Cite

show abstract

“…In fact, the last part of the network in Ref. [] is in a 2D way. However, our transfer learning strategy is in a 3D way, and it is the first time that the idea has applied to classification task, meaning that the network structure is a kind of “image‐input/numeral‐output” pattern.…”

Section: Methodsmentioning

confidence: 99%

Automatic classification of lung nodule candidates based on a novel 3D convolution network and knowledge transferred from a 2D network

Wang

Zhou²,

He³

et al. 2019

Medical Physics

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Objective In the automatic lung nodule detection system, the authenticity of a large number of nodule candidates needs to be judged, which is a classification task. However, the variable shapes and sizes of the lung nodules have posed a great challenge to the classification of candidates. To solve this problem, we propose a method for classifying nodule candidates through three‐dimensional (3D) convolution neural network (ConvNet) model which is trained by transferring knowledge from a multiresolution two‐dimensional (2D) ConvNet model. Methods In this scheme, a novel 3D ConvNet model is preweighted with the weights of the trained 2D ConvNet model, and then the 3D ConvNet model is trained with 3D image volumes. In this way, the knowledge transfer method can make 3D network easier to converge and make full use of the spatial information of nodules with different sizes and shapes to improve the classification accuracy. Results The experimental results on 551 065 pulmonary nodule candidates in the LUNA16 dataset show that our method gains a competitive average score in the false‐positive reduction track in lung nodule detection, with the sensitivities of 0.619 and 0.642 at 0.125 and 0.25 FPs per scan, respectively. Conclusions The proposed method can maintain satisfactory classification accuracy even when the false‐positive rate is extremely small in the face of nodules of different sizes and shapes. Moreover, as a transfer learning idea, the method to transfer knowledge from 2D ConvNet to 3D ConvNet is the first attempt to carry out full migration of parameters of various layers including convolution layers, full connection layers, and classifier between different dimensional models, which is more conducive to utilizing the existing 2D ConvNet resources and generalizing transfer learning schemes.

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“…Transferring information from related data has been shown to be useful in dealing with the problem of lacking sufficient training data [28], [45], [46]. However, domain shift caused by the data distribution difference between the datasets is a common problem impacting the efficiency and performance of transfer learning.…”

Section: A Boundary-weighted Knowledge Transfermentioning

confidence: 99%

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

Zhu

Yan

2020

IEEE Trans. Med. Imaging

154

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Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. Recently, as deep learning, especially convolutional neural networks (CNNs), emerging as the best performed methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced than ever. Since large-scale dataset is one of the critical components for the success of deep learning, lack of sufficient training data makes it difficult to fully train complex CNNs. To tackle the above challenges, in this paper, we propose a boundary-weighted domain adaptive neural network (BOWDA-Net). To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss is proposed. Furthermore, an advanced boundary-weighted transfer leaning approach is introduced to address the problem of small medical imaging datasets. We evaluate our proposed model on three different MR prostate datasets. The experimental results demonstrate that the proposed model is more sensitive to object boundaries and outperformed other state-of-the-art methods.

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Correction for “3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2D Trained Network” [Jun 18 1522-1534]

Cited by 24 publications

References 32 publications

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

Automatic classification of lung nodule candidates based on a novel 3D convolution network and knowledge transferred from a 2D network

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

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