Real-time estimation of lung deformation from body surface using a general CoordConv CNN.

Background And Objective: Estimating the three-dimensional (3D) deformation of the lung is important for accurate dose delivery in radiotherapy and precise surgical guidance in lung surgery navigation. Additional 4D-CT information is often required to eliminate the effect of individual variations and obtain a more accurate estimation of lung deformation. However, this results in increased radiation dose.

Exhaustive matching of 3D/2D coronary artery structure based on imperfect segmentations.

Purpose: The 3D/2D coronary artery registration technique has been developed for the guidance of the percutaneous coronary intervention. It introduces the absent 3D structural information by fusing the pre-operative computed tomography angiography (CTA) volume with the intra-operative X-ray coronary angiography (XCA) image. To conduct the registration, an accurate matching of the coronary artery structures extracted from the two imaging modalities is an essential step.

SR: Shape and Semantics-Based Selective Regularization for Explainable Continual Segmentation Across Multiple Sites.

In clinical practice, it is desirable for medical image segmentation models to be able to continually learn on a sequential data stream from multiple sites, rather than a consolidated dataset, due to storage cost and privacy restrictions. However, when learning on a new site, existing methods struggle with a weak memorizability for previous sites with complex shape and semantic information, and a poor explainability for the memory consolidation process. In this work, we propose a novel Shape and Semantics-based Selective Regularization ( [Formula: see text]) method for explainable cross-site continual segmentation to maintain both shape and semantic knowledge of previously learned sites.

Design, development and evaluation of an ergonomically designed dual-use mechanism for robot-assisted cardiovascular intervention.

Purpose: Robot-assisted cardiovascular intervention has been recently developed, which enables interventionists to avoid x-ray radiation and improve their comfort. However, there are still some challenges in the robotic design, such as the inability of the interventionist to freely perform natural clinical techniques and the limited motion travel of the interventional tool. To overcome these challenges, this paper proposes an ergonomically designed dual-use mechanism for cardiovascular intervention (DMCI).

Spine Segmentation with Multi-view GCN and Boundary Constraint.

Multi-class segmentation of vertebrae and inter-vertebral discs (IVDs) is crucial for the diagnosis and treatment of spinal diseases. However, it is still a challenge due to similarities between neighboring vertebrae of a subject and differences among the IVDs from different subjects. In this paper, we propose a novel spine segmentation framework to achieve automatic segmentation of vertebrae and IVDs in MR images.

Integrated Treatment Planning in Percutaneous Microwave Ablation of Lung Tumors.

Microwave ablation (MWA) is a clinically widespread minimally invasive treatment method for lung tumors. Preoperative planning plays a vital role in MWA therapy. However, previous planning methods are far from satisfactory in clinical practice because they only one-sidedly consider the surgical path or energy parameters of an MWA surgery.

An automatic framework for estimating the pose of the catheter distal section using a coarse-to-fine network.

Background And Objective: During percutaneous coronary intervention procedures, generally only 2D X-ray images are provided. The consequent lack of depth perception makes it difficult for interventionists to visually estimate the pose of medical tools inside the vasculature, especially for novices. Although some automatic methods have been developed to aid interventionists, it is still a challenging task to obtain stable and accurate pose estimation.

CAR-Net: A Deep Learning-Based Deformation Model for 3D/2D Coronary Artery Registration.

Percutaneous coronary intervention is widely applied for the treatment of coronary artery disease under the guidance of X-ray coronary angiography (XCA) image. However, the projective nature of XCA causes the loss of 3D structural information, which hinders the intervention. This issue can be addressed by the deformable 3D/2D coronary artery registration technique, which fuses the pre-operative computed tomography angiography volume with the intra-operative XCA image.

Target localization during respiration motion based on LSTM: A pilot study on robotic puncture system.

Background: In the needle biopsy, the respiratory motion causes the displacement of thoracic-abdominal soft tissues, which brings great difficulty to accurate localization. Based on internal target motion and external marker motion, the existing methods need to establish a correlation model or a prediction model to compensate the respiratory movement, which can hardly achieve required accuracy in clinic use due to the complexity of the internal tissue motion.

Methods: In order to improve the tracking accuracy and reduce the number of models, we propose a framework for target localization based on long short-term memory (LSTM) method.

Deformable Registration of Coronary Arteries with Topological Constraints for Image-Guided Vascular Interventions.

2D/3D registration of preoperative computed tomography angiography with intra-operative X-ray angiography improves image guidance in percutaneous coronary intervention. However, previous registration methods are inaccurate and time-consuming due to simple deformation and iterative optimization, respectively. In this paper, we propose a novel method for non-rigid registration of coronary arteries based on a point set registration network, which predicts the complex deformation field directly without iterative optimization.

Generation of a local lung respiratory motion model using a weighted sparse algorithm and motion prior-based registration.

Respiration-introduced tumor location uncertainty is a challenge in lung percutaneous interventions, especially for the respiratory motion estimation of the tumor and surrounding vessel structures. In this work, a local motion modeling method is proposed based on whole-chest computed tomography (CT) and CT-fluoroscopy (CTF) scans. A weighted sparse statistical modeling (WSSM) method that can accurately capture location errors for each landmark point is proposed for lung motion prediction.

A statistical weighted sparse-based local lung motion modelling approach for model-driven lung biopsy.

Purpose: Lung biopsy is currently the most effective procedure for cancer diagnosis. However, respiration-induced location uncertainty presents a challenge in precise lung biopsy. To reduce the medical image requirements for motion modelling, in this study, local lung motion information in the region of interest (ROI) is extracted from whole chest computed tomography (CT) and CT-fluoroscopy scans to predict the motion of potentially cancerous tissue and important vessels during the model-driven lung biopsy process.

Automatic detection of coronary artery stenosis by convolutional neural network with temporal constraint.

Coronary artery disease (CAD) is a major threat to human health. In clinical practice, X-ray coronary angiography remains the gold standard for CAD diagnosis, where the detection of stenosis is a crucial step. However, detection is challenging due to the low contrast between vessels and surrounding tissues as well as the complex overlap of background structures with inhomogeneous intensities.

Spline curve deformation model with prior shapes for identifying adhesion boundaries between large lung tumors and tissues around lungs in CT images.

Purpose: Automated segmentation of lung tumors attached to anatomic structures such as the chest wall or mediastinum remains a technical challenge because of the similar Hounsfield units of these structures. To address this challenge, we propose herein a spline curve deformation model that combines prior shapes to correct large spatially contiguous errors (LSCEs) in input shapes derived from image-appearance cues.The model is then used to identify the adhesion boundaries between large lung tumors and tissue around the lungs.

Prediction of late displacement of the globe in orbital blowout fractures.

Purpose: To establish a linear measuring method in computed tomographic (CT) images to predict the displacement of the globe late after orbital blowout fracture.

Methods: Subjects were retrospectively included. Inclusion criteria were as follows: (1) adult subjects (≥18 years old at the time of trauma); (2) unilateral orbital medial-wall and/or floor fractures; (3) CT examination at least 30 days after trauma.

A novel robotic system for vascular intervention: principles, performances, and applications.

Purpose: This paper describes the design, principles, performances, and applications of a novel image-guided master-slave robotic system for vascular intervention (VI), including the performance evaluation and in vivo trials.

Methods: Based on the peer-to-peer (P2P) remote communication system, the kinetics analysis, the sliding-mode neural network self-adaptive control model and the feedback system, this new robotic system can accomplish in real time a number of VI operations, including guidewire translation and rotation, balloon catheter translation, and contrast agent injection. The master-slave design prevents surgeons from being exposed to X-ray radiation, which means that they are not required to wear a heavy lead suit.

Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization.

For myocardial infarction (MI) patients, delayed enhancement (DE) and T2-weighted cardiovascular magnetic resonance imaging (CMR) can play significant roles in diagnosis, prognosis and therapeutic strategy evaluation. However, the non-rigid registration between different CMR sequences is particularly challenging and prevents the use of multi-sequence image analysis. In this article, we propose an approach for segmenting T2 and DE CMR simultaneously with cross-constrained shape and shape discrepancy compensation.

Measurement of Intra-Orbital Structures in Normal Chinese Adults Based on a Three-Dimensional Coordinate System.

Purpose Of The Study: This study was to establish a three-dimensional (3D) coordinate system and to study the normal dimensions of intra-orbital structures in Chinese adults.

Materials And Methods: One hundred and forty-five adult Chinese were selected from patients who had undergone cranio-facial computed tomography scans with diagnosis other than orbital or ocular abnormality. An orbital 3D coordinate system was built on the basis of the scans.

A novel remote-controlled robotic system for cerebrovascular intervention.

Background: In cerebrovascular intervention (CVI), the use of robots has considerable advantages over conventional surgery. This study introduces a remote-controlled robotic system, including the first in vivo proof-of-concept trial.

Methods: The robotic system uses a master-slave control strategy.

Myocardium segmentation from DE MRI with guided random walks and sparse shape representation.

Purpose: For patients with myocardial infarction (MI), delayed enhancement (DE) cardiovascular magnetic resonance imaging (MRI) is a sensitive and well-validated technique for the detection and visualization of MI. The myocardium viability assessment with DE MRI is important in diagnosis and treatment management, where myocardium segmentation is a prerequisite. However, few academic works have focused on automated myocardium segmentation from DE images.

Vesselness-constrained robust PCA for vessel enhancement in x-ray coronary angiograms.

Effective vessel enhancement in x-ray coronary angiograms (XCA) is essential for the diagnosis of coronary artery disease, yet challenged by complex background structures of varying intensities as well as motion patterns. As a typical layer-separation method, robust principal component analysis (RPCA) has been proposed to automatically improve vessel visibility via sparse and low-rank decomposition. However, the attenuated motion of vessels in x-ray angiograms leads to the unsatisfactory vessel enhancement performance of the decomposition framework.

Minimization of annotation work: diagnosis of mammographic masses via active learning.

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance.

Flow instability detected in ruptured versus unruptured cerebral aneurysms at the internal carotid artery.

Flow instability has emerged as a new hemodynamic metric hypothesized to have potential value in assessing the rupture risk of cerebral aneurysms. However, diverse findings have been reported in the literature. In the present study, high-resolution hemodynamic simulations were performed retrospectively on 35 aneurysms (10 ruptured & 25 unruptured) located at the internal carotid artery (ICA).