Coronary artery disease (CAD) is the leading cause of death globally. The 3D fusion of coronary X-ray angiography (XA) and optical coherence tomography (OCT) provides complementary information to appreciate coronary anatomy and plaque morphology. This significantly improve CAD diagnosis and prognosis by enabling precise hemodynamic and computational physiology assessments. The challenges of fusion lie in the potential misalignment caused by the foreshortening effect in XA and non-uniform acquisition of OCT pullback. Moreover, the need for reconstructions of major bifurcations is technically demanding. This paper proposed an automated 3D fusion framework AutoFOX, which consists of deep learning model TransCAN for 3D vessel alignment. The 3D vessel contours are processed as sequential data, whose features are extracted and integrated with bifurcation information to enhance alignment via a multi-task fashion. TransCAN shows the highest alignment accuracy among all methods with a mean alignment error of 0.99 ± 0.81 mm along the vascular sequence, and only 0.82 ± 0.69 mm at key anatomical positions. The proposed AutoFOX framework uniquely employs an advanced side branch lumen reconstruction algorithm to enhance the assessment of bifurcation lesions. A multi-center dataset is utilized for independent external validation, using the paired 3D coronary computer tomography angiography (CTA) as the reference standard. Novel morphological metrics are proposed to evaluate the fusion accuracy. Our experiments show that the fusion model generated by AutoFOX exhibits high morphological consistency with CTA. AutoFOX framework enables automatic and comprehensive assessment of CAD, especially for the accurate assessment of bifurcation stenosis, which is of clinical value to guiding procedure and optimization.
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http://dx.doi.org/10.1016/j.media.2024.103432 | DOI Listing |
Neural Netw
December 2024
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China. Electronic address:
Currently, for obtaining more accurate counts, existing methods primarily utilize RGB images combined with features of complementary modality (X-modality) for counting. However, designing a model that can adapt to various sensors is still an unsolved issue due to the differences in features between different modalities. Therefore, this paper proposes a unified fusion framework called CMFX for RGB-X crowd counting.
View Article and Find Full Text PDFBMC Bioinformatics
December 2024
Department of Information Management and Big Data Center, Peking University Third Hospital, Beijing, 100191, China.
Backgrounds: Diagnostic prediction is a central application that spans various medical specialties and scenarios, sequential diagnosis prediction is the process of predicting future diagnoses based on patients' historical visits. Prior research has underexplored the impact of irregular intervals between patient visits on predictive models, despite its significance.
Method: We developed the Multi-task Fusion Visit Interval for Sequential Diagnosis Prediction (MISDP) framework to address this research gap.
Med Image Anal
December 2024
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; Department of Cardiovascular Medicine, University of Oxford, OX39DU, UK. Electronic address:
IEEE Open J Eng Med Biol
November 2024
Biomedical Information Processing LabÉcole de Technologie Supérieure, University of Québec Montréal H3C 1K3 Canada.
Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation. In this study, we propose a hybrid approach to effectively segment common occlusions encountered in remote monitoring applications within PICUs.
View Article and Find Full Text PDFIEEE Open J Eng Med Biol
October 2024
Division of Cardiac Surgery, Beth Israel Deaconess Medical CenterHarvard Medical School Boston MA 02115 USA.
To detect Hypertrophic Cardiomyopathy (HCM) from multiple views of Echocardiogram (cardiac ultrasound) videos. we propose , a novel framework that performs binary classification (HCM vs. no HCM) of echocardiogram videos directly using an ensemble of state-of-the-art deep VAR architectures models (SlowFast and I3D), and fuses their predictions using majority averaging ensembling.
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