AIMC Topic: Image Processing, Computer-Assisted
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Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based on F-FDG PET radiomics and clinical factors.
Annals of nuclear medicine
Mar 29, 2025
OBJECTIVE: This study aimed to develop and validate a nomogram combining F-FDG PET radiomics and clinical factors to non-invasively predict bone marrow involvement (BMI) in patients with lymphoma.
A segment anything model-guided and match-based semi-supervised segmentation framework for medical imaging.
Medical physics
Mar 29, 2025
BACKGROUND: Semi-supervised segmentation leverages sparse annotation information to learn rich representations from combined labeled and label-less data for segmentation tasks. The Match-based framework, by using the consistency constraint of segment...
Guided ultrasound acquisition for nonrigid image registration using reinforcement learning.
Medical image analysis
Mar 29, 2025
We propose a guided registration method for spatially aligning a fixed preoperative image and untracked ultrasound image slices. We exploit the unique interactive and spatially heterogeneous nature of this application to develop a registration algori...
Artificial intelligence for tumor [F]FDG-PET imaging: Advancement and future trends-part I.
Seminars in nuclear medicine
Mar 29, 2025
The advent of sophisticated image analysis techniques has facilitated the extraction of increasingly complex data, such as radiomic features, from various imaging modalities, including [F]FDG PET/CT, a well-established cornerstone of oncological imag...
Deep structural brain imaging via computational three-photon microscopy.
Journal of biomedical optics
Mar 29, 2025
SIGNIFICANCE: High-resolution optical imaging at significant depths is challenging due to scattering, which impairs image quality in living matter with complex structures. We address the need for improved imaging techniques in deep tissues.
Dual-Filter Cross Attention and Onion Pooling Network for Enhanced Few-Shot Medical Image Segmentation.
Sensors (Basel, Switzerland)
Mar 29, 2025
Few-shot learning has demonstrated remarkable performance in medical image segmentation. However, existing few-shot medical image segmentation (FSMIS) models often struggle to fully utilize query image information, leading to prototype bias and limit...
ULST: U-shaped LeWin Spectral Transformer for virtual staining of pathological sections.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 28, 2025
At present, pathological section staining faces several challenges, including complex sample preparation and stringent infrastructure requirements. Virtual staining methods utilizing deep neural networks to automatically generate stained images are g...
Perspectives: Comparison of deep learning segmentation models on biophysical and biomedical data.
Biophysical journal
Mar 28, 2025
Deep learning-based approaches are now widely used across biophysics to help automate a variety of tasks including image segmentation, feature selection, and deconvolution. However, the presence of multiple competing deep learning architectures, each...
ResTransUNet: A hybrid CNN-transformer approach for liver and tumor segmentation in CT images.
Computers in biology and medicine
Mar 28, 2025
BACKGROUND AND OBJECTIVE: Accurate medical tumor segmentation is critical for early diagnosis and treatment planning, significantly improving patient outcomes. This study aims to enhance liver and tumor segmentation from CT and liver images by develo...
Footwork recognition and trajectory tracking in track and field based on image processing.
Scientific reports
Mar 28, 2025
In track and field sports, footwork can greatly affect the effect and performance of sports. Accurate footwork can effectively improve the performance of professional athletes, and for ordinary trainers, it can reduce the probability of training inju...
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