AIMC Topic: Image Processing, Computer-Assisted

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Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging.

Physics in medicine and biology
Dual-tracer positron emission tomography (PET) is a promising technique to measure the distribution of two tracers in the body by a single scan, which can improve the clinical accuracy of disease diagnosis and can also serve as a research tool for sc...

Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence: a prospective, single-blind, 3-parallel-group, randomized, single-center trial.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: EGD is the most vital procedure for the diagnosis of upper GI lesions. We aimed to compare the performance of unsedated ultrathin transoral endoscopy (U-TOE), unsedated conventional EGD (C-EGD), and sedated C-EGD with or without ...

Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach.

The American journal of pathology
The application of deep learning for the detection of lymph node metastases on histologic slides has attracted worldwide attention due to its potentially important role in patient treatment and prognosis. Despite this attention, false-positive predic...

Technical and Clinical Factors Affecting Success Rate of a Deep Learning Method for Pancreas Segmentation on CT.

Academic radiology
PURPOSE: Accurate pancreas segmentation has application in surgical planning, assessment of diabetes, and detection and analysis of pancreatic tumors. Factors that affect pancreas segmentation accuracy have not been previously reported. The purpose o...

Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation.

IEEE transactions on neural networks and learning systems
Biomedical image segmentation is lately dominated by deep neural networks (DNNs) due to their surpassing expert-level performance. However, the existing DNN models for biomedical image segmentation are generally highly parameterized, which severely i...

A superpixel-driven deep learning approach for the analysis of dermatological wounds.

Computer methods and programs in biomedicine
BACKGROUND: The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU, that combines deep learning models wi...

Machine learning for radiomics-based multimodality and multiparametric modeling.

The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of...
Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in onco...

Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network.

Medical physics
PURPOSE: In order to attain anatomical models, surgical guides and implants for computer-assisted surgery, accurate segmentation of bony structures in cone-beam computed tomography (CBCT) scans is required. However, this image segmentation step is of...

Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter.

Scientific reports
Current histological and anatomical analysis techniques, including fluorescence in situ hybridisation, immunohistochemistry, immunofluorescence, immunoelectron microscopy and fluorescent fusion protein, have revealed great distribution diversity of m...

Technical considerations of multi-parametric tissue outcome prediction methods in acute ischemic stroke patients.

Scientific reports
Decisions regarding acute stroke treatment rely heavily on imaging, but interpretation can be difficult for physicians. Machine learning methods can assist clinicians by providing tissue outcome predictions for different treatment approaches based on...