Coronary artery extraction is a crucial prerequisite for computer-aided diagnosis of coronary artery disease. Accurately extracting the complete coronary tree remains challenging due to several factors, including presence of thin distal vessels, tortuous topological structures, and insufficient contrast. These issues often result in over-segmentation and under-segmentation in current segmentation methods. To address these challenges, we propose a topology-preserving three-stage framework for fully-connected coronary artery extraction. This framework includes vessel segmentation, centerline reconnection, and missing vessel reconstruction. First, we introduce a new centerline enhanced loss in the segmentation process. Second, for the broken vessel segments, we further propose a regularized walk algorithm to integrate distance, probabilities predicted by a centerline classifier, and directional cosine similarity, for reconnecting the centerlines. Third, we apply implicit neural representation and implicit modeling, to reconstruct the geometric model of the missing vessels. Experimental results show that our proposed framework outperforms existing methods, achieving Dice scores of 88.53% and 85.07%, with Hausdorff Distances (HD) of 1.07 mm and 1.63 mm on ASOCA and PDSCA datasets, respectively. Code will be available at https://github.com/YH-Qiu/CorSegRec.

Coronary artery segmentation is critical for coronary artery disease diagnosis but challenging due to its tortuous course with numerous small branches and inter-subject variations. Most existing studies ignore important anatomical information and vascular topologies, leading to less desirable segmentation performance that usually cannot satisfy clinical demands. To deal with these challenges, in this paper we propose an anatomy- and topology-preserving two-stage framework for coronary artery segmentation. The proposed framework consists of an anatomical dependency encoding (ADE) module and a hierarchical topology learning (HTL) module for coarse-to-fine segmentation, respectively. Specifically, the ADE module segments four heart chambers and aorta, and thus five distance field maps are obtained to encode distance between chamber surfaces and coarsely segmented coronary artery. Meanwhile, ADE also performs coronary artery detection to crop region-of-interest and eliminate foreground-background imbalance. The follow-up HTL module performs fine segmentation by exploiting three hierarchical vascular topologies, i.e., key points, centerlines, and neighbor connectivity using a multi-task learning scheme. In addition, we adopt a bottom-up attention interaction (BAI) module to integrate the feature representations extracted across hierarchical topologies. Extensive experiments on public and in-house datasets show that the proposed framework achieves state-of-the-art performance for coronary artery segmentation.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting high-dimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.

In full or arbitrary field-of-view (FOV) 3D CT imaging, obtaining an accurate per-voxel segmentation for complete large and small bones remains an unsolved and challenging problem. The difficulty lies in the notable variation in appearance and position observed among cortical bones, marrow and pathologies. To approach this problem, several studies have employed active shape models and atlas models. In this paper, we argue that a bottom-up approach, defined by classifying and grouping supervoxels, is another viable technique. Moreover, it can be integrated into a conditional random field (CRF) representation. Our approach consists of the following steps: first, an input CT volume is decomposed into supervoxels, in order to ensure very high bone boundary recall. Supervoxels are generated via a robust process of conservative region partitioning and recursive region merging. In order to maximize sparsity and classification efficiency, we use a Bayesian sparse linear classifier to compute and optimize middle-level image features. Next, we disambiguate the CRF unary potentials via contextualized optimization by pooling over selective supervoxel pairs. Finally, we adopt a pairwise support vector machine (SVM) model to learn the CRF pairwise potential in a fully supervised manner. We evaluate our method quantitatively on 137 low-resolution, low-contrast CT volumes with severe imaging noise, among which various bone pathologies are represented. Our system proves to be efficient; it achieves a clinically significant segmentation accuracy level (Dice Coefficient 98.2%).

Accurate segmentation of the spinal canals in computed tomography (CT) images is an important task in many related studies. In this paper, we propose an automatic segmentation method and apply it to our highly challenging image cohort that is acquired from multiple clinical sites and from the CT channel of the PET-CT scans. To this end, we adapt the interactive random-walk solvers to be a fully automatic cascaded pipeline. The automatic segmentation pipeline is initialized with robust voxelwise classification using Haar-like features and probabilistic boosting tree. Then, the topology of the spinal canal is extracted from the tentative segmentation and further refined for the subsequent random-walk solver. In particular, the refined topology leads to improved seeding voxels or boundary conditions, which allow the subsequent random-walk solver to improve the segmentation result. Therefore, by iteratively refining the spinal canal topology and cascading the random-walk solvers, satisfactory segmentation results can be acquired within only a few iterations, even for cases with scoliosis, bone fractures and lesions. Our experiments validate the capability of the proposed method with promising segmentation performance, even though the resolution and the contrast of our dataset with 110 patient cases (90 for testing and 20 for training) are low and various bone pathologies occur frequently.

The automatic extraction and labeling of the rib centerlines is a useful yet challenging task in many clinical applications. In this paper, we propose a new approach integrating rib seed point detection and template matching to detect and identify each rib in chest CT scans. The bottom-up learning based detection exploits local image cues and top-down deformable template matching imposes global shape constraints. To adapt to the shape deformation of different rib cages whereas maintain high computational efficiency, we employ a Markov Random Field (MRF) based articulated rigid transformation method followed by Active Contour Model (ACM) deformation. Compared with traditional methods that each rib is individually detected, traced and labeled, the new approach is not only much more robust due to prior shape constraints of the whole rib cage, but removes tedious post-processing such as rib pairing and ordering steps because each rib is automatically labeled during the template matching. For experimental validation, we create an annotated database of 112 challenging volumes with ribs of various sizes, shapes, and pathologies such as metastases and fractures. The proposed approach shows orders of magnitude higher detection and labeling accuracy than state-of-the-art solutions and runs about 40 seconds for a complete rib cage on the average.

The problem of learning a proper distance or similarity metric arises in many applications such as content-based image retrieval. In this work, we propose a boosting algorithm, MetricBoost, to learn the distance metric that preserves the proximity relationships among object triplets: object i is more similar to object j than to object k. Metric-Boost constructs a positive semi-definite (PSD) matrix that parameterizes the distance metric by combining rank-one PSD matrices. Different options of weak models and combination coefficients are derived. Unlike existing proximity preserving metric learning which is generally not scalable, MetricBoost employs a bipartite strategy to dramatically reduce computation cost by decomposing proximity relationships over triplets into pair-wise constraints. Met-ricBoost outperforms the state-of-the-art on two real-world medical problems: 1. identifying and quantifying diffuse lung diseases; 2. colorectal polyp matching between different views, as well as on other benchmark datasets.

We present a novel method to solve sign ambiguity for phase demodulation from a single interferometric image that possibly contains closed fringes. The problem is formulated in a binary pairwise energy minimization framework based on phase gradient orientation continuity. The objective function is non-submodular and therefore its minimization is an NP-hard problem, for which we devise a multigrid hierarchy of quadratic pseudoboolean optimization problems that can be improved iteratively to approximate the optimal solutions. Compared with traditional path-following phase demodulation methods, the new approach does not require any heuristic scanning strategy, it is not subject to the propagation of error, and the extension to three dimensional fringe patterns is straightforward. A set of experiments with synthetic data and real prelens tear film interferometric images of the human eye demonstrate the effectiveness and robustness of the proposed algorithm in comparison with existing state-of-the-art phase demodulation methods.

The automatic detection of lung nodules attached to other pulmonary structures is a useful yet challenging task in lung CAD systems. In this paper, we propose a stratified statistical learning approach to recognize whether a candidate nodule detected in CT images connects to any of three other major lung anatomies, namely vessel, fissure and lung wall, or is solitary with background parenchyma. First, we develop a fully automated voxel-by-voxel labeling/segmentation method of nodule, vessel, fissure, lung wall and parenchyma given a 3D lung image, via a unified feature set and classifier under conditional random field. Second, the generated Class Probability Response Maps (PRM) by voxel-level classifiers, are used to form the so-called pairwise Probability Co-occurrence Maps (PCM) which encode the spatial contextual correlations of the candidate nodule, in relation to other anatomical landmarks. Based on PCMs, higher level classifiers are trained to recognize whether the nodule touches other pulmonary structures, as a multi-label problem. We also present a new iterative fissure structure enhancement filter with superior performance. For experimental validation, we create an annotated database of 784 subvolumes with nodules of various sizes, shapes, densities and contextual anatomies, and from 239 patients. High accuracy of multi-class voxel labeling is achieved 89.3% ∼ 91.2%. The Area under ROC Curve (AUC) of vessel, fissure and lung wall connectivity classification reaches 0.8676, 0.8692 and 0.9275, respectively.

The computer aided diagnosis (CAD) problems of detecting potentially diseased structures from medical images are typically distinguished by the following challenging characteristics: extremely unbalanced data between negative and positive classes; stringent real-time requirement of online execution; multiple positive candidates generated for the same malignant structure that are highly correlated and spatially close to each other. To address all these problems, we propose a novel learning formulation to combine cascade classification and multiple instance learning (MIL) in a unified min-max framework, leading to a joint optimization problem which can be converted to a tractable quadratically constrained quadratic program and efficiently solved by block-coordinate optimization algorithms. We apply the proposed approach to the CAD problems of detecting pulmonary embolism and colon cancer from computed tomography images. Experimental results show that our approach significantly reduces the computational cost while yielding comparable detection accuracy to the current state-of-the-art MIL or cascaded classifiers. Although not specifically designed for balanced MIL problems, the proposed method achieves superior performance on balanced MIL benchmark data such as MUSK and image data sets.

We propose a dimension reduction technique named Resilient Subclass Discriminant Analysis (RSDA) for high dimensional classification problems. The technique iteratively estimates the subclass division by embedding the Fisher Discriminant Analysis (FDA) with Expectation-Maximization (EM) in Gaussian Mixture Models (GMM). The new method maintains the adaptability of SDA to a wide range of data distributions by approximating the distribution of each class as a mixture of Gaussians, and provides superior feature selection performance to SDA with modified EM clustering that estimates a posteriori probability of latent variables in lower-dimensional Fisher's discriminant space, which also improves the robustness in problems of small training datasets compared with conventional EM algorithm. Extensive experiments and comparison results against other well-known Discriminant Analysis (DA) methods are presented using synthetic data, benchmark datasets as well as a real computational vision problem.

In this article, we present a new scheme that combines the turbo product codes with a simple single carrier frequency domain based equalization algorithm. By using transmit diversity in frequency selective Rayleigh fading channels, the system can resist intersymbol interference effectively. Simulation results show that the proposed system offers diversity gain of more than 6 dB.