Right ventricular segmentation in cardiac MRI with moving mesh correspondences.

This study investigates automatic propagation of the right ventricle (RV) endocardial and epicardial boundaries in 4D (3D+time) magnetic resonance imaging (MRI) sequences. Based on a moving mesh (or grid generation) framework, the proposed algorithm detects the endocardium and epicardium within each cardiac phase via point-to-point correspondences. The proposed method has the following advantages over prior RV segmentation works: (1) it removes the need for a time-consuming, manually built training set; (2) it does not make prior assumptions as to the intensity distributions or shape; (3) it provides a sequence of corresponding points over time, a comprehensive input that can be very useful in cardiac applications other than segmentation, e.g., regional wall motion analysis; and (4) it is more flexible for congenital heart disease where the RV undergoes high variations in shape. Furthermore, the proposed method allows comprehensive RV volumetric analysis over the complete cardiac cycle as well as automatic detections of end-systolic and end-diastolic phases because it provides a segmentation for each time step. Evaluated quantitatively over the 48-subject data set of the MICCAI 2012 RV segmentation challenge, the proposed method yielded an average Dice score of 0.84±0.11 for the epicardium and 0.79±0.17 for the endocardium. Further, quantitative evaluations of the proposed approach in comparisons to manual contours over 23 infant hypoplastic left heart syndrome patients yielded a Dice score of 0.82±0.14, which demonstrates the robustness of the algorithm

Computer-assisted detection of cemento-enamel junction in intraoral ultrasonographs

The cemento-enamel junction (CEJ) is an important reference point for various clinical measurements in oral health assessment. Identifying CEJ in ultrasound images is a challenging task for dentists. In this study, a computer-assisted detection method is proposed to identify the CEJ in ultrasound images, based on the curvature change of the junction outlining the upper edge of the enamel and cementum at the cementum–enamel intersection. The technique consists of image preprocessing steps for image enhancement, segmentation, and edge detection to locate the boundary of the enamel and cementum. The effects of the image preprocessing and the sizes of the bounding boxes enclosing the CEJ were studied. For validation, the algorithm was applied to 120 images acquired from human volunteers. The mean difference of the best performance between the proposed method and the two raters’ measurements was an average of 0.25 mm with reliability ≥ 0.98. The proposed method has the potential to assist dental professionals in CEJ identification on ultrasonographs to provide better patient care

MyoPS A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/)

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field

High-Resolution Stereoscopic Visualization of Pediatric Echocardiography Data on Microsoft HoloLens 2

Three-dimensional ultrasound offers volumetric images and detailed anatomical data for medical diagnosis and treatment planning. It is a key tool in the medical field to obtain a comprehensive view of the body. Ordinary two-dimensional displays do not provide depth perception and are not suitable for representing volumetric data, necessitating the use of more sophisticated visualization methods. Virtual and augmented reality (AR) displays can be used to improve the visualization of medical images, allowing for more natural interaction with the environment. This study proposes custom software developed using the Unity3D platform to render high-resolution 3D echocardiography (3DE) on the Microsoft HoloLens 2, providing an immersive AR experience for medical professionals. This research focuses on three-dimensional echocardiography in children and uses a phantom heart model to mimic a pulsating heart. The volume rendering algorithm utilizes the ray-marching technique, enabling direct volume rendering of high-quality volumetric models. To maintain a satisfactory frame rate, a Holographic Remoting approach is employed to reduce latency and enhance network transmission speed, utilizing the resources of a personal computer (PC). The custom software developed offers an intuitive and interactive user interface that allows medical professionals to manipulate and explore 3DE images effectively. The interaction includes the ability to slice, modify the intensity range, and alter the voxel density. The experimental evaluations demonstrated that it is possible to produce high-quality real-time display with HoloLens 2 and a PC-based remote rendering system, allowing intuitive control and exploration of 3DE. Overall, this research highlights the potential of AR rendering offered through Microsoft HoloLens 2 to advance pediatric 3DE rendering for medical professionals to enhance their decision-making and understanding of medical datasets

AI-assisted mole detection for online dermatology triage in telemedicine settings

Skin moles are one of the most critical conditions for early diagnosis of severe conditions such as melanoma. Early identification of moles has become crucial nowadays, primarily due to malignant melanoma, a dangerous type of skin cancer. Most of the recent advances concerning this domain of dermatology deal mostly with either classifying moles as benign or malignant or with methods that help delineate moles from skin images. However, there are minimal sources of exploration in just determining whether moles are present in a given query image. In this paper, we have employed the latest state-of-the-art neural networks to identify the presence of moles in dermatological images uploaded by patients on an online teledermatology platform. This filter flags if a mole is detected, which can be employed as a triage system that helps the dermatologist diagnose and ease the follow-up treatment procedure in a physical setting if the patient has a mole in their image. A comparative study of the prediction performance of the different models has been provided for different performance metrics of interest. The results presented in this paper have been obtained from two sets of data, consisting of more than 26,000 clinical pictures with different conditions combined. Multiple experiments using different models yielded a macro average recall value as high as 0.955, along with overall accuracy and macro average precision values of 0.962 and 0.958, respectively