AIMC Topic: Image Processing, Computer-Assisted

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Nonlinear Spiking Neural Systems for Thermal Image Semantic Segmentation Networks.

International journal of neural systems
Thermal and RGB images exhibit significant differences in information representation, especially in low-light or nighttime environments. Thermal images provide temperature information, complementing the RGB images by restoring details and contextual ...

Semiautomated segmentation of breast tumor on automatic breast ultrasound image using a large-scale model with customized modules.

Scientific reports
To verify the capability of the Segment Anything Model for medical images in 3D (SAM-Med3D), tailored with low-rank adaptation (LoRA) strategies, in segmenting breast tumors in Automated Breast Ultrasound (ABUS) images. This retrospective study colle...

Feasibility study of a general model for synthetic CT generation in MRI-guided extracranial radiotherapy.

Biomedical physics & engineering express
This study aims to investigate the feasibility of a single general model to synthesize CT images across body sites, thorax, abdomen, and pelvis, to support treatment planning for MRI-only radiotherapy. A total of 157 patients who received MRI-guided ...

A Point Cloud Generation Network for Automatic Prediction of Postoperative Maxillofacial Soft Tissue.

Annals of biomedical engineering
PURPOSE: In the planning of maxillofacial surgery, accurately evaluating the postoperative soft tissue area is crucial. This allows doctors to provide patients with better morphological recovery while ensuring the restoration of normal functional are...

Pancreas segmentation using AI developed on the largest CT dataset with multi-institutional validation and implications for early cancer detection.

Scientific reports
Accurate and fully automated pancreas segmentation is critical for advancing imaging biomarkers in early pancreatic cancer detection and for biomarker discovery in endocrine and exocrine pancreatic diseases. We developed and evaluated a deep learning...

AI-assisted diffuse correlation tomography for identifying breast cancer.

Journal of biomedical optics
SIGNIFICANCE: Diffuse correlation tomography (DCT) is an emerging technique for the noninvasive measurement of breast microvascular blood flow, whereas its capability to categorize benign and malignant breast lesions has not been extensively validate...

A monocular endoscopic image depth estimation method based on a window-adaptive asymmetric dual-branch Siamese network.

Scientific reports
Minimally invasive surgery involves entering the body through small incisions or natural orifices, using a medical endoscope for observation and clinical procedures. However, traditional endoscopic images often suffer from low texture and uneven illu...

Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images.

Scientific reports
PCOS (Poly-Cystic Ovary Syndrome) is a multifaceted disorder that often affects the ovarian morphology of women of their reproductive age, resulting in the development of numerous cysts on the ovaries. Ultrasound imaging typically diagnoses PCOS, whi...

Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models.

Scientific reports
Alzheimer's disease (AD) is a neurodegenerative disorder that affects memory and cognitive functions. Manual diagnosis is prone to human error, often leading to misdiagnosis or delayed detection. MRI techniques help visualize the fine tissues of the ...

Are Diffusion Models Effective Good Feature Extractors for MRI Discriminative Tasks?

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Diffusion models (DMs) excel in pixel-level and spatial tasks and are proven feature extractors for 2D image discriminative tasks when pretrained. However, their capabilities in 3D MRI discriminative tasks remain largely untapped. This study...