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Cylindrical holography has received widespread attention due to its unique 360° viewing zone. To achieve commercial quality requirements, introducing stochastic gradient descent (SGD) is a potential approach for computer-generated cylindrical holography (CGCH). However, SGD applied to CGCH suffers from both slow convergence speed and unstable convergence, severely impacting its application. To address these issues, a preloaded SGD method with skip connection is proposed for fast calculation of cylindrical holograms in this paper. Preloaded-SGD (PSGD) exhibits a significant enhancement in convergence speed compared to the conventional SGD. Furthermore, the skip connection prevents oscillations from occurring by directly connecting the input and output, which is highly beneficial for obtaining high-quality holograms in the later stages of convergence. Numerical simulations demonstrate the effectiveness of our proposed method. PSGD with skip connection(SC-PSGD) achieves a 6.3-fold acceleration over conventional SGD. Notably, our proposed method has broad application prospects in cylindrical holographic displays and 3D displays.
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http://dx.doi.org/10.1364/OE.529920 | DOI Listing |
Med Biol Eng Comput
September 2025
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300072, China.
Surgical instrument segmentation plays an important role in robotic autonomous surgical navigation systems as it can accurately locate surgical instruments and estimate their posture, which helps surgeons understand the position and orientation of the instruments. However, there are still some problems affecting segmentation accuracy, like insufficient attention to the edges and center of surgical instruments, insufficient usage of low-level feature details, etc. To address these issues, a lightweight network for surgical instrument segmentation in gastrointestinal (GI) endoscopy (GESur_Net) is proposed.
View Article and Find Full Text PDFMed Biol Eng Comput
September 2025
College of Medicine and Biomedical Information Engineering, Northeastern University, 110169, Shenyang, China.
Recognition of tumors is very important in clinical practice and radiomics; however, the segmentation task currently still needs to be done manually by experts. With the development of deep learning, automatic segmentation of tumors is gradually becoming possible. This paper combines the molecular information from PET and the pathology information from CT for tumor segmentation.
View Article and Find Full Text PDFBMC Med Imaging
September 2025
School of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-Ro, Dongjak-Gu, Seoul, 06974, Republic of Korea.
Biomed Phys Eng Express
September 2025
Zhejiang University, zhejiang, Hangzhou, Zhejiang, 310058, CHINA.
Medical image segmentation faces significant challenges in cross-domain scenarios due to variations in imaging protocols and device-specific artifacts. While existing methods leverage either spatial-domain features or global frequency transforms (e.g.
View Article and Find Full Text PDFMagn Reson Med
September 2025
Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland.
Purpose: Removing water residual signals from MRS spectra is crucial for accurate metabolite quantification. However, currently available algorithms are computationally intensive and time-consuming, limiting their clinical applicability. This work aims to propose and validate two novel pipelines for fast water residual removal in MRS.
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