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DenseSeg: joint learning for semantic segmentation and landmark detection using dense image-to-shape representation.
Int J Comput Assist Radiol Surg
January 2025
Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Purpose: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for segmentation, it falls short in landmark detection, a strength of shape-based approaches.
Methods: In this work, we propose a dense image-to-shape representation that enables the joint learning of landmarks and semantic segmentation by employing a fully convolutional architecture.
Deep Learning-Based Multi-View Projection Synthesis Approach for Improving the Quality of Sparse-View CBCT in Image-Guided Radiotherapy.
J Imaging Inform Med
January 2025
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
While radiation hazards induced by cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) can be reduced by sparse-view sampling, the image quality is inevitably degraded. We propose a deep learning-based multi-view projection synthesis (DLMPS) approach to improve the quality of sparse-view low-dose CBCT images. In the proposed DLMPS approach, linear interpolation was first applied to sparse-view projections and the projections were rearranged into sinograms; these sinograms were processed with a sinogram restoration model and then rearranged back into projections.
View Article and Find Full Text PDFA novel domain feature disentanglement method for multi-target cross-domain mechanical fault diagnosis.
ISA Trans
January 2025
State Key Laboratory of Computer-Aided Design and Computer Graphics, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Intelligent Rescue Equipment for Collapse Accidents, Ministry of Emergency Management, Hangzhou, 310030, China; Zhejiang Laboratory, Hangzhou, 311121, China. Electronic address:
Existing cross-domain mechanical fault diagnosis methods primarily achieve feature alignment by directly optimizing interdomain and category distances. However, this approach can be computationally expensive in multi-target scenarios or fail due to conflicting objectives, leading to decreased diagnostic performance. To avoid these issues, this paper introduces a novel method called domain feature disentanglement.
View Article and Find Full Text PDFLead-Grouped Multi-Stage Learning for Myocardial Infarction Localization.
Methods
January 2025
School of Design, Hunan University, Changsha, 410082, China. Electronic address:
The electrocardiogram (ECG) is a ubiquitous medical diagnostic tool employed to localize myocardial infarction (MI) that is characterized by abnormal waveform patterns on the ECG. MI is a serious cardiovascular disease, and accurate, timely diagnosis is crucial for preventing severe outcomes. Current ECG analysis methods mainly rely on intra- and inter-lead feature extraction, but most models overlook the medical knowledge relevant to disease diagnosis.
View Article and Find Full Text PDFAdaptive functioning and relationship to visual behavior in children with cerebral/cortical visual impairment.
J AAPOS
January 2025
Keck School of Medicine of USC, Los Angeles, California; Division of Ophthalmology, Children's Hospital Los Angeles, Los Angeles, California. Electronic address:
Background: Cerebral/cortical visual impairment (CVI) is a leading cause of pediatric visual impairment in developed countries and is associated with neurologic conditions that may impair adaptive functioning or skills required to perform everyday tasks (eg, communication, socialization, and daily living skills). Adaptive behavior in children with CVI has not been systematically studied, and the relationship between visual function and adaptive function in CVI is unknown.
Methods: We prospectively recruited 49 children with CVI (mean age, 4 ± 3 years).
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