- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Automatic segmentation of echocardiographic images using a shifted windows vision transformer architecture.

Echocardiography is one the most commonly used imaging modalities for the diagnosis of congenital heart disease. Echocardiographic image analysis is crucial to obtaining accurate cardiac anatomy information. Semantic segmentation models can be used to precisely delimit the borders of the left ventricle, and allow an accurate and automatic identification of the region of interest, which can be extremely useful for cardiologists.

Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.

Purpose: Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option to plan the outcome of surgery on a patient-based model. However, it requires considerable computing power and time, which may discourage its use.

Cumulative Dosage of Intrathecal Chemotherapy Agents Predicts White Matter Integrity in Long-Term Survivors of Acute Lymphoblastic Leukemia: A PETALE Study.

Acute lymphoblastic leukemia (ALL) stands as the most prevalent form of pediatric cancer in North America, with a current five-year survival rate of 85%. While more children achieved ALL remission and transition into adulthood, the prevalence of long-term treatment-related effects, especially neurocognitive sequelae, remains significant. This study pursues two objectives.

Attention-gated U-Net networks for simultaneous axial/sagittal planes segmentation of injured spinal cords.

Magnetic resonance imaging is currently the gold standard for the evaluation of spinal cord injuries. Automatic analysis of these injuries is however challenging, as MRI resolutions vary for different planes of analysis and physiological features are often distorted around these injuries. This study proposes a new CNN-based segmentation method in which information is exchanged between two networks analyzing the scans from different planes.

Automated detection of regions of interest in cartridge case images using deep learning.

This paper explores a deep-learning approach to evaluate the position of circular delimiters in cartridge case images. These delimiters define two regions of interest (ROI), corresponding to the breech face and the firing pin impressions, and are placed manually or by an image-processing algorithm. This positioning bears a significant impact on the performance of the image-matching algorithms for firearm identification, and an automated evaluation method would be beneficial to any computerized system.

Unsupervised synthesis of realistic coronary artery X-ray angiogram.

Purpose: Medical image analysis suffers from a sparsity of annotated data necessary in learning-based models. Cardiorespiratory simulators have been developed to counter the lack of data. However, the resulting data often lack realism.

Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks.

To develop a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple long short-term memory (LSTM) model.The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine transformation matrices, which provide the displacement information of contrasted moving objects (arteries and medical devices) in a sequence. The displacement information includes translation, rotation, shearing, and scaling in 2D.

Navigation guidance for ventricular septal defect closure in heart phantoms.

Purpose: Transesophageal echocardiography (TEE) is the preferred imaging modality in a hybrid procedure used to close ventricular septal defects (VSDs). However, the limited field of view of TEE hinders the maneuvering of surgical instruments inside the beating heart. This study evaluates the accuracy of a method that aims to support navigation guidance in the hybrid procedure.

Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions.

Background: Navigation guidance in cardiac interventions is provided by X-ray angiography. Cumulative radiation exposure is a serious concern for pediatric cardiac interventions.

Purpose: A generative learning-based approach is proposed to predict X-ray angiography frames to reduce the radiation exposure for pediatric cardiac interventions while preserving the image quality.

Intravascular imaging of coronary artery: Bridging the gap between clinical needs and technical advances.

Coronary artery disease is the leading cause of mortality worldwide. Almost seven million deaths are reported each year due to coronary disease. Coronary artery events in the adult are primarily due to atherosclerosis with seventy-five percent of the related mortality caused by plaque rupture.

Automatic bone maturity grading from EOS radiographs in Adolescent Idiopathic Scoliosis.

Adolescent Idiopathic Scoliosis (AIS) is a deformation of the spine and it is routinely diagnosed using posteroanterior and lateral radiographs. The Risser sign used in skeletal maturity assessment is commonly accepted in AIS patient's management. However, the Risser sign is subject to inter-observer variability and it relies mainly on the observation of ossification on the iliac crests.

Design of heart phantoms for ultrasound imaging of ventricular septal defects.

Purpose: Ventricular septal defects (VSDs) are common congenital heart malformations. Echocardiography used during VSD hybrid cardiac procedures requires extensive training for image acquisition and interpretation. Cardiac surgery simulators with heart phantoms have shown usefulness for such training, but they are limited in visualization and characterization of complex VSD.

Convolutional Neural Networks for Automatic Risser Stage Assessment.

Purpose: To develop an automatic method for the assessment of the Risser stage using deep learning that could be used in the management panel of adolescent idiopathic scoliosis (AIS).

Materials And Methods: In this institutional review board approved-study, a total of 1830 posteroanterior radiographs of patients with AIS (age range, 10-18 years, 70% female) were collected retrospectively and graded manually by six trained readers using the United States Risser staging system. Each radiograph was preprocessed and cropped to include the entire pelvic region.

A deep learning-based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images.

Purpose: Coronary artery events are mainly associated with atherosclerosis in adult population, which is recognized as accumulation of plaques in arterial wall tissues. Optical Coherence Tomography (OCT) is a light-based imaging system used in cardiology to analyze intracoronary tissue layers and pathological formations including plaque accumulation. This state-of-the-art catheter-based imaging system provides intracoronary cross-sectional images with high resolution of 10-15 µm.

Real-time biomechanics using the finite element method and machine learning: Review and perspective.

Purpose: The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real-time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time.

A freehand ultrasound framework for spine assessment in 3D: a preliminary study.

X-ray imaging is currently the gold standard for the assessment of spinal deformities. The purpose of this study is to evaluate a freehand 3D ultrasound system for volumetric reconstruction of the spine. A setup consisting of an ultrasound scanner with a linear transducer, an electromagnetic measuring system and a workstation was used.

Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric MRI.

Segmentation of the left ventricle in magnetic resonance imaging (MRI) is important for assessing cardiac function. We present DT-GAN, a generative adversarial network (GAN) segmentation approach for the identification of the left ventricle in pediatric MRI. Segmentation of the left ventricle requires a large amount of annotated data; generating such data can be time-consuming and subject to observer variability.

Model-free Cardiorespiratory Motion Prediction from X-ray Angiography Sequence with LSTM Network.

We present a novel model-free approach for cardiorespiratory motion prediction from X-ray angiography time series based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN). Cardiorespiratory motion prediction is defined as a problem of estimating the future displacement of the coronary vessels in the next image frame in an X-ray angiography sequence. The displacement of the vessels is represented as a sequence of 2D affine transformation matrices allowing 2D X-ray registrations in a sequence.

Epistemic Uncertainty Modeling for Vessel Segmentation.

X-ray angiograms are currently the gold-standard in percutaneous guidance during cardiovascular interventions. However, due to lack of contrast, to overlapping artifacts and to the rapid dilution of the contrast agent, they remain difficult to analyze either by cardiologists, or automatically by computers. Providing, a general yet accurate multi-arteries segmentation method along with the uncertainty linked to those segmentations would not only ease the analysis of medical imaging by cardiologists, but also provide a required pre-processing of the data for tasks ranging from 3D reconstruction to motion tracking of arteries.

An automatic diagnostic system of coronary artery lesions in Kawasaki disease using intravascular optical coherence tomography imaging.

Intravascular optical coherence tomography (IV-OCT) is a light-based imaging modality with high resolution, which employs near-infrared light to provide tomographic intracoronary images. Morbidity caused by coronary heart disease is a substantial cause of acute coronary syndrome and sudden cardiac death. The most common intracoronay complications caused by coronary artery disease are intimal hyperplasia, calcification, fibrosis, neovascularization and macrophage accumulation, which require efficient prevention strategies.

CycleGAN for style transfer in X-ray angiography.

Purpose: We aim to perform generation of angiograms for various vascular structures as a mean of data augmentation in learning tasks. The task is to enhance the realism of vessels images generated from an anatomically realistic cardiorespiratory simulator to make them look like real angiographies.

Methods: The enhancement is performed by applying the CycleGAN deep network for transferring the style of real angiograms acquired during percutaneous interventions into a data set composed of realistically simulated arteries.

Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features.

Intra-slice motion correction is an important step for analyzing volume variations and pathological formations from intravascular imaging. Optical coherence tomography (OCT) has been recently introduced for intravascular imaging and assessment of coronary artery disease. Two-dimensional (2-D) cross-sectional OCT images of coronary arteries play a crucial role to characterize the internal structure of the tissues.

Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression.

Background: The progression of the spinal curve represents one of the major concerns in the assessment of Adolescent Idiopathic Scoliosis (AIS). The prediction of the shape of the spine from the first visit could guide the management of AIS and provide the right treatment to prevent curve progression.

Method: In this work, we propose a novel approach based on a statistical generative model to predict the shape variation of the spinal curve from the first visit.