Purpose: We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four-dimensional (4D) CT. The method could prove useful in IMRT of lung cancer. Methods: We modified the conventional graph-cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data. Active shape models (ASM) with signed distance function were used to capture the shape prior information, preventing unwanted surrounding tissues from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend three-dimensional (3D) segmentation to 4D by warping a prior shape model through time. The algorithm has been applied to segmentation of wellcircumscribed, vascularized, and juxtapleural lung nodules from respiratory gated CT data. Results: In all cases, 4D segmentation and tracking for five phases of high-resolution CT data took approximately 10 min on a PC workstation with AMD Phenom II and 32 GB of memory. The method was trained based on 500 breath-held 3D CT data from the LIDC data base 1 and was tested on 17 4D lung nodule CT datasets consisting of 85 volumetric frames. The validation tests resulted in an average Dice Similarity Coefficient (DSC) = 0.68 for all test data. An important by-product of the method is quantitative volume measurement from 4D CT from end-inspiration to end-expiration which will also have important diagnostic value. Conclusion: The algorithm performs robust segmentation of lung nodules from 4D CT data. Signed distance ASM provides the shape prior information which based on the iterative graph-cuts framework is adaptively refined to best fit the input data, preventing unwanted surrounding tissue from merging with the segmented object.

SCoTS captures a sparse representation of shapes in an input image through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. For the problem of lung nodule segmentation in X-ray CT, SCoTS offers a unified framework, capable of segmenting nodules of all types. Experimental validations are demonstrated on 542 3-D lung nodule images from the LIDC-IDRI database. Despite its generality, SCoTS is competitive with domain specific state of the art methods for lung nodule segmentation.

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Purpose: Multiview two-dimensional (2D) convolutional neural networks (CNNs) and three-dimensional (3D) CNNs have been successfully used for analyzing volumetric data in many state-of-the-art medical imaging applications. We propose an alternative modular framework that analyzes volumetric data with an approach that is analogous to radiologists' interpretation, and apply the framework to reduce false positives that are generated in computer-aided detection (CADe) systems for pulmonary nodules in thoracic computed tomography (CT) scans. Methods: In our approach, a deep network consisting of 2D CNNs first processes slices individually. The features extracted in this stage are then passed to a recurrent neural network (RNN), thereby modeling consecutive slices as a sequence of temporal data and capturing the contextual information across all three dimensions in the volume of interest. Outputs of the RNN layer are weighed before the final fully connected layer, enabling the network to scale the importance of different slices within a volume of interest in an end-to-end training framework. Results: We validated the proposed architecture on the false positive reduction track of the lung nodule analysis (LUNA) challenge for pulmonary nodule detection in chest CT scans, and obtained competitive results compared to 3D CNNs. Our results show that the proposed approach can encode the 3D information in volumetric data effectively by achieving a sensitivity >0.8 with just 1/8 false positives per scan. Conclusions: Our experimental results demonstrate the effectiveness of temporal analysis of volumetric images for the application of false positive reduction in chest CT scans and show that state-ofthe-art 2D architectures from the literature can be directly applied to analyzing volumetric medical data. As newer and better 2D architectures are being developed at a much faster rate compared to 3D architectures, our approach makes it easy to obtain state-of-the-art performance on volumetric data using new 2D architectures.

Bag of visual words model has recently attracted much attention from computer vision society because of its notable success in analysing images and exploring their content. This study improves this model by utilising the adjacency information between words. To explore this information, a binary tree structure is constructed from the visual words in order to model the isa relationships in the vocabulary. Informative nodes of this tree are extracted by using the χ 2 criterion and are used to capture the adjacency information of visual words. This approach is a simple and computationally effective way for modelling the spatial relations of visual words, which improves the image classification performance. The authors evaluated our method for visual classification of three known datasets: 15 natural scenes, Caltech-101 and Graz-01.

Most of the Deep Neural Networks (DNNs) based CT image denoising literature shows that DNNs outperform traditional iterative methods in terms of metrics such as the RMSE, the PSNR and the SSIM. In many instances, using the same metrics, the DNN results from low-dose inputs are also shown to be comparable to their high-dose counterparts. However, these metrics do not reveal if the DNN results preserve the visibility of subtle lesions or if they alter the CT image properties such as the noise texture.Accordingly, in this work, we seek to examine the image quality of the DNN results from a holistic viewpoint for low-dose CT image denoising. First, we build a library of advanced DNN denoising architectures. This library is comprised of denoising architectures such as the DnCNN, U-Net, Red-Net, GAN, etc. Next, each network is modeled, as well as trained, such that it yields its best performance in terms of the PSNR and SSIM. As such, data inputs (e.g. training patch-size, reconstruction kernel) and numeric-optimizer inputs (e.g. minibatch size, learning rate, loss function) are accordingly tuned. Finally, outputs from thus trained networks are further subjected to a series of CT bench testing metrics such as the contrast-dependent MTF, the NPS and the HU accuracy. These metrics are employed to perform a more nuanced study of the resolution of the DNN outputs' low-contrast features, their noise textures, and their CT number accuracy to better understand the impact each DNN algorithm has on these underlying attributes of image quality.

Lung tumor segmentation is important for therapy in the radiation treatment of patients with thoracic malignancies. In this paper, we describe a 4D image segmentation method based on graph-cuts optimization, shape prior and optical flow. Due to small size, the location, and low contrast between the tumor and the surrounding tissue, tumor segmentation in 3D+t is challenging. We performed 4D lung tumor segmentation in 5 patients, and in each case compared the results with the expert-delineated lung nodules. In each case, 4D image segmentation took approximately ten minutes on a PC with AMD Phenom II and 32GB of memory for segmenting tumor in five phases of lung CT data.

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