AIMC Topic: Image Processing, Computer-Assisted

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A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRI.

Magnetic resonance imaging
Accurate segmentation of renal tissues is an essential step for renal perfusion estimation and postoperative assessment of the allograft. Images are usually manually labeled, which is tedious and prone to human error. We present an image analysis met...

Automatic orbital segmentation using deep learning-based 2D U-net and accuracy evaluation: A retrospective study.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
The purpose of this study was to verify whether the accuracy of automatic segmentation (AS) of computed tomography (CT) images of fractured orbits using deep learning (DL) is sufficient for clinical application. In the surgery of orbital fractures, m...

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry.

Nature methods
Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the...

Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models.

Laboratory investigation; a journal of technical methods and pathology
Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enablin...

A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques.

Computers in biology and medicine
The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor cancers. However, the use of existing methods for eliminating speckle noise can cause the loss of image...

Enhancing Robustness of Medical Image Segmentation Model with Neural Memory Ordinary Differential Equation.

International journal of neural systems
Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates subs...

Deep learning for tumor margin identification in electromagnetic imaging.

Scientific reports
In this work, a novel method for tumor margin identification in electromagnetic imaging is proposed to optimize the tumor removal surgery. This capability will enable the visualization of the border of the cancerous tissue for the surgeon prior or du...

Backdoor attack and defense in federated generative adversarial network-based medical image synthesis.

Medical image analysis
Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research and augment medical datasets. Training generative adversarial neural networks (GANs) usually require large ...

A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images.

Medical physics
PURPOSE: Deep learning-based networks have become increasingly popular in the field of medical image segmentation. The purpose of this research was to develop and optimize a new architecture for automatic segmentation of the prostate gland and normal...

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human dise...