AIMC Topic: Image Processing, Computer-Assisted

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LiDSCUNet++: A lightweight depth separable convolutional UNet++ for vertebral column segmentation and spondylosis detection.

Research in veterinary science
Accurate computer-aided diagnosis systems rely on precise segmentation of the vertebral column to assist physicians in diagnosing various disorders. However, segmenting spinal disks and bones becomes challenging in the presence of abnormalities and c...

Artificial intelligence (AI)-driven morphological assessment of zebrafish larvae for developmental toxicity chemical screening.

Aquatic toxicology (Amsterdam, Netherlands)
Screening chemicals using the zebrafish embryo developmental toxicity assay requires visual assessment of larval morphological changes based on images by experienced screeners. The process is time-consuming and prone to subjectivity. However, deep le...

Efficient few-shot medical image segmentation via self-supervised variational autoencoder.

Medical image analysis
Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, ...

Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI.

Medical image analysis
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational an...

Learning contrast and content representations for synthesizing magnetic resonance image of arbitrary contrast.

Medical image analysis
Magnetic Resonance Imaging (MRI) produces images with different contrasts, providing complementary information for clinical diagnoses and research. However, acquiring a complete set of MRI sequences can be challenging due to limitations such as lengt...

Automatic deep learning segmentation of mandibular periodontal bone topography on cone-beam computed tomography images.

Journal of dentistry
OBJECTIVES: This study evaluated the performance of a multi-stage Segmentation Residual Network (SegResNet)-based deep learning (DL) model for the automatic segmentation of cone-beam computed tomography (CBCT) images of patients with stage III and IV...

Automated Microbubble Discrimination in Ultrasound Localization Microscopy by Vision Transformer.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound localization microscopy (ULM) has revolutionized microvascular imaging by breaking the acoustic diffraction limit. However, different ULM workflows depend heavily on distinct prior knowledge, such as the impulse response and empirical sele...

A myocardial reorientation method based on feature point detection for quantitative analysis of PET myocardial perfusion imaging.

Computer methods and programs in biomedicine
OBJECTIVE: Reorienting cardiac positron emission tomography (PET) images to the transaxial plane is essential for cardiac PET image analysis. This study aims to design a convolutional neural network (CNN) for automatic reorientation and evaluate its ...

UltrasOM: A mamba-based network for 3D freehand ultrasound reconstruction using optical flow.

Computer methods and programs in biomedicine
BACKGROUND: Three-dimensional (3D) ultrasound (US) reconstruction is of significant value in clinical diagnosis, characterized by its safety, portability, low cost, and high real-time capabilities. 3D freehand ultrasound reconstruction aims to elimin...

Learning-Based Classification of B- and T-Cell Lymphoma on Histopathological Images: A Multicenter Study.

European journal of haematology
Lymphoma, a clonal malignancy from lymphocytes, includes diverse subtypes requiring distinct immunohistochemical stains for accurate diagnosis. Limited biopsy specimens often restrict the use of multiple stains, complicating diagnostic workflows. Lym...