AIMC Topic: Image Processing, Computer-Assisted
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Leveraging deep learning for improving parameter extraction from perfusion MR images: A narrative review.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Apr 11, 2025
BACKGROUND: Perfusion magnetic resonance imaging (MRI) is a non-invasive technique essential for assessing tissue microcirculation and perfusion dynamics. Various perfusion MRI techniques like Dynamic Contrast-Enhanced (DCE), Dynamic Susceptibility C...
Optimized classification of dental implants using convolutional neural networks and pre-trained models with preprocessed data.
BMC oral health
Apr 11, 2025
OBJECTIVE: This study evaluates the performance of various classifiers and pre-trained models for dental implant state classification using preprocessed radiography images with masks.
Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.
Biomedical physics & engineering express
Apr 11, 2025
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...
Generating realistic single-cell images from CellProfiler representations.
Medical image analysis
Apr 10, 2025
High-throughput imaging techniques acquire large amounts of images efficiently. These images contain rich biological information including cellular processes. A common method to analyse them is to encode them into quantitative representation vectors....
μGlia-Flow, an automatic workflow for microglia segmentation and classification.
Journal of neuroscience methods
Apr 10, 2025
BACKGROUND: Microglia are important immune cells in the central nervous system, playing a key role in various pathological processes. The morphological diversity of microglia is closely linked to the development of brain diseases, yet accurate segmen...
FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI.
NeuroImage
Apr 10, 2025
Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neu...
MDAL: Modality-difference-based active learning for multimodal medical image analysis via contrastive learning and pointwise mutual information.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 9, 2025
Multimodal medical images reveal different characteristics of the same anatomy or lesion, offering significant clinical value. Deep learning has achieved widespread success in medical image analysis with large-scale labeled datasets. However, annotat...
DIPathMamba: A domain-incremental weakly supervised state space model for pathology image segmentation.
Medical image analysis
Apr 9, 2025
Accurate segmentation of pathology images plays a crucial role in digital pathology workflow. However, two significant issues exist with the present pathology image segmentation methods: (i) Most fully supervised models rely on dense pixel-level anno...
Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images.
Medical physics
Apr 9, 2025
BACKGROUND: Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemorrhages, using only healthy training data. Recent advancements in generative models have achieved suc...
Portal dose image prediction using Monte Carlo generated transmission energy fluence maps of dynamic radiotherapy treatment plans: a deep learning approach.
Biomedical physics & engineering express
Apr 9, 2025
This work aims to develop and investigate the feasibility of a hybrid model combining Monte Carlo (MC) simulations and deep learning (DL) to predict electronic portal imaging device (EPID) images based on MC-generated exit phase space energy fluence ...
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