Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images

Cheema, Muhammad Nadeem; Nazir, Abdul; Sheng, Bin; Li, Ping; Qin, Jing; Feng, David Dagan

doi:10.1109/tbme.2019.2894123

Cited by 18 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the simulated reduced dose levels included in this library, we believe that any differences that may exist between simulated and measured data have negligible impact on algorithms developed using the provided lower‐dose data. This belief is supported by the successful use of the data in numerous publications 26–34 …”

Section: Discussionmentioning

confidence: 82%

“…A cycle‐consistent adversarial network (CycleGAN) for low‐dose CT image denoising without paired CT images for training 33 A residual CNN for liver extraction from low‐dose CT images 34 …”

Section: Discussionmentioning

confidence: 99%

“…A residual CNN for liver extraction from low-dose CT images. 34 As additional examples of the value of these data, within our own research program and clinical practice, we have used these and other data to.…”

mentioning

confidence: 99%

“…This belief is supported by the successful use of the data in numerous publications. [26][27][28][29][30][31][32][33][34] The dataset is provided in a paired fashion at both routine dose and simulated lower dose, which is ideal for many techniques involving supervised learning. Many noise reduction techniques trained based on these paired datasets have demonstrated great success in terms of reducing image noise and improving image quality.…”

mentioning

confidence: 99%

“…Many noise reduction techniques trained based on these paired datasets have demonstrated great success in terms of reducing image noise and improving image quality. [26][27][28][29][30][31][32][33][34] However, given the recent success of unsupervised low-dose CT reconstruction, it is desirable also to have unpaired low-dose and full-dose dataset available, which is a topic of interest for future development. Meanwhile, the current paired dataset provided in this work can still be used to meet that purpose using appropriate arrangement of training cases (e.g., organize the cases in an unpaired fashion), which is beneficial as the full-dose images can be used as references even if they are not used in the training and testing.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Low‐dose CT image and projection dataset

et al. 2020

View full text Add to dashboard Cite

To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. Acquisition and Validation Methods: The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise-insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. Data Format and Usage Notes: Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive (

show abstract

Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Low‐dose CT image and projection dataset

et al. 2020

View full text Add to dashboard Cite

show abstract

Hybrid‐attention densely connected U‐Net with GAP for extracting livers from CT volumes

Chen

Wang

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Purpose: Liver segmentation is an important step in the clinical treatment of liver cancer, and accurate and automatic liver segmentation methods are extremely important. U-Net has been used as the benchmark for many medical segmentation networks, but it cannot fully utilize low-resolution information and global contextual information. To solve these problems, we propose a new network architecture named the hybrid-attention densely connected U-Net (HDU-Net). Methods: The proposed HDU-Net has three main changes relative to U-Net, as follows: (1) It uses a densely connected structure and dilated convolution to achieve feature reuse and avoid information loss. (2) A global average pooling block is proposed to further augment the receptive field and improve the segmentation accuracy of the network for small or disconnected liver regions. (3) By combining the spatial attention and channel attention mechanisms, a hybrid attention structure is proposed to replace the skip connection component to filter and integrate low-resolution information. Results: Experiments conducted on the LITS2017, 3Dircadb and Sliver07 datasets show that the proposed model can segment the liver accurately and effectively. Dice scores reach 96.5%, 96.18%, and 97.57% on these datasets, respectively, constituting results that are superior to many previously proposed methods. Conclusions:The experimental liver segmentation results have demonstrated that our proposed network provides improved segmentation performance in comparison with other networks. The experimental results without postprocessing confirmed that our network solves the oversegmentation and undersegmentation problems to some extent. The proposed model is effective, robust, and efficient in terms of liver segmentation without requiring extensive training time or a very large dataset.

show abstract