Purpose: This paper presents a novel 3D multimodal registration strategy to fuse 3D real-time echocardiography images with cardiac cine MRI images. This alignment is performed in a saliency space, which is designed to maximize similarity between the two imaging modalities. This fusion improves the quality of the available information.
Methods: The method performs in two steps: temporal and spatial registrations. A temporal alignment is firstly achieved by nonlinearly matching pairs of correspondences between the two modalities using a dynamic time warping. A temporal registration is then carried out by applying nonrigid transformations in a common saliency space where normalized cross correlation between temporal pairs of salient volumes is maximized.
Results: The alignment performance was evaluated with a set of 18 subjects, 3 with cardiomyopathies and 15 healthy, by computing the Dice score and Hausdorff distance with respect to manual delineations of the left ventricle cavity in both modalities. A Dice score and Hausdorff distance of [Formula: see text] and [Formula: see text], respectively, were obtained. In addition, the deformation field was estimated by quantifying its foldings, obtaining a 98% of regularity in the deformation field.
Conclusions: The 3D multimodal registration strategy presented is performed in a saliency space. Unlike state-of-the-art methods, the presented one takes advantage of the temporal information of the heart to construct this common space, ending up with two well-aligned modalities and regular deformation fields. This preliminary study was evaluated on heterogeneous data composed of two different datasets, healthy and pathological cases, showing similar performances in both cases. Future work will focus on testing the presented strategy in a larger dataset with a balanced number of classes.
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http://dx.doi.org/10.1007/s11548-019-02087-w | DOI Listing |
Comput Methods Programs Biomed
December 2024
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Zhejiang, Hangzhou, China.
Background: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.
Objective: This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).
Neural Netw
December 2024
State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, 710071, Shanxi, China.
Hyperspectral anomaly detection (HAD) aims to localize pixel points whose spectral features differ from the background. HAD is essential in scenarios of unknown or camouflaged target features, such as water quality monitoring, crop growth monitoring and camouflaged target detection, where prior information of targets is difficult to obtain. Existing HAD methods aim to objectively detect and distinguish background and anomalous spectra, which can be achieved almost effortlessly by human perception.
View Article and Find Full Text PDFAutism Res
December 2024
Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
Individuals with autism spectrum condition (ASC) are suggested to experience difficulties with categorization and generalization. However, empirical studies have mainly focused on one process at a time, and neglected underlying neural mechanisms. Here, we investigated categorization and generalization at a behavioral and neural level in 38 autistic and 38 neurotypical (NT) adults.
View Article and Find Full Text PDFBiol Methods Protoc
November 2024
Department of Psychological and Brain Sciences, Computational Neuroscience and Vision Lab, Center for Systems Neuroscience, and Program for Neuroscience, Boston University, Boston, MA, 02215, United States.
PeerJ Comput Sci
October 2024
Faculty of Art and Design, Jiangxi Institute of Fashion Technology, Nanchang, Jiangxi, China.
This study delves deeply into exploring the artistic value of traditional Chinese painting (TCP) and aims to bridge the gap between its fundamental characteristics and the realm of human emotions. To achieve this, a novel convolutional neural network (CNN)-based classification model for TCP emotions is proposed. By thoroughly analyzing the distinct emotional mapping relationships inherent in TCP, a comprehensive framework is developed.
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