Segmentation of the right ventricle (RV) in magnetic resonance imaging (MRI) sequences is critical for assessing RV function. However, manual segmentation involves processing hundreds of images per patient, making it a tedious and timeconsuming process. Recently, deep convolutional neural networks have emerged as an effective solution for automating RV segmentation in MRI sequences, substantially reducing manual workload. Accurate segmentation of the RV is crucial for reliable clinical applications. In this study, we demonstrate that transfer learning using a pre-trained segmentation model from the Medical Open Network for Artificial Intelligence (MONAI) Model Zoo significantly improves segmentation accuracy, as measured by the Dice similarity coefficient (DSC) and$\mathbf{9 5}^{\text {th }}$percentile Hausdorff distance (HD95) scores, compared to manual annotations from medical experts. Our approach increased DSC-based segmentation accuracy from 74.93 % (pre-trained MONAI Zoo model) and 83.15 % (same architecture trained on our data) to 84.91 % on 1,994 test images acquired from seven patients. Furthermore, it outperformed a state-of-the-art self-configuring network, nnU-Net, which achieved an accuracy of 81.98 % on the same dataset. This study demonstrates the effectiveness of transfer learning in improving segmentation accuracy for the proposed task.

Effective visualization of three-dimensional (3D) medical images is essential for planning medical procedures, as it directly impacts diagnostic precision and enhances patient understanding. High-quality 3D visualization enables physicians to make informed decisions, supports surgeons in planning complex procedures, and helps patients better understand their conditions and treatment options. However, commonly used headmounted display (HMD) devices such as the Meta Quest and Microsoft HoloLens often cause discomfort, eye strain, and communication challenges, and their complex setup can limit practical use in clinical settings. The recently developed, glassesfree Looking Glass Factory 16” Spatial Display presents a compelling alternative, capable of delivering high-quality holographic images without requiring HMDs, making it a promising solution for clinical visualization. This study investigates using the Looking Glass 16” Spatial Display to visualize 3D pediatric cardiac CT scans in DICOM format, demonstrating its potential to improve accessibility and usability in clinical imaging. Clinical DICOM images are converted into MetaImages (RAW images with MHD headers) using 3DSlicer software and rendered with a volume rendering algorithm to create detailed volumetric models. These 3D images are then displayed on the Looking Glass device through the Unity3D platform, with an NVIDIA GeForce RTX 4060 Ti graphics card optimizing frame rates, reducing latency, and supporting real-time image control on a personal computer (PC). Experimental evaluations confirm the feasibility of producing high-quality, real-time displays on the Looking Glass 16” Spatial Display, offering clinicians an intuitive and efficient interface. This research highlights the potential of the Looking Glass 16” Spatial Display to enhance 3D medical image visualization, particularly for cardiac CT applications. By providing a more precise, accessible imaging method, this technology could significantly improve clinical decision-making and deepen medical professionals understanding of complex anatomical data.

Echocardiography plays an important role in the assessment of cardiovascular diseases. The lack of ionizing radiation and portability make it one of the safest imaging modalities. Although two-dimensional echocardiography is widely used to obtain the motion of the heart structures in real-time, three-dimensional (3D) echocardiography allows for scanning of the heart in 3D with unlimited postprocessing geometries compared to 2D. However, the feasibility is limited because of speckle noise, poor quality, limited field of view and missing anatomical structures. The entire heart cannot be imaged in a single 3D echocardiography scan in most cases, and further improvements are needed to solve the problem. This study proposes a point-based rigid registration followed by B-spline non-rigid registration to align 4D echocardiography images obtained from different sonographic windows. The approach was tested on scans obtained from three volunteer participants. The accuracy of registration was visually and quantitatively assessed by delineating the left ventricle in each scan and computing the Dice score overlap metric and the Hausdorff distance mutual proximity measure between the first scan and the rest. The overall results indicate that the proposed registration approach improves the alignment of the images compared to the original scans.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

Cardiac segmentation from magnetic resonance imaging (MRI) is one of the essential tasks to analyze the anatomy and function of the heart for the assessment and diagnosis of cardiac diseases. However, manual annotation is difficult and time consuming. This study proposes a novel end-to-end supervised cardiac MRI segmentation framework based on a diffeomorphic deformable registration that can segment the left ventricle from 2D and 3D images or volumes. In order to represent the actual cardiac deformation, the methodology parameterizes the transformation using radial and rotational components, computed using a deep learning approach The method was evaluated over three different data sets and showed significant improvements compared to exacting learning and non-learning based methods in terms of the Dice score and Hausdorff distance metrics.

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.

Real-time rendering of static volumetric data is generally known to be a memory and computationally intensive process. With the advance of graphic hardware, especially GPU, it is now possible to do this using desktop computers. However, with the evolution of real-time CT and MRI technologies, volumetric rendering is an even bigger challenge. The first one is how to reduce the data transmission between the main memory and the graphic memory. The second one is how to efficiently take advantage of the time redundancy which exists in time-varying volumetric data. We proposed an optimized compression scheme that explores the time redundancy as well as space redundancy of time-varying volumetric data. The compressed data is then transmitted to graphic memory and directly rendered by the GPU, reducing significantly the data transfer between main memory and graphic memory.