AIMC Topic: Image Processing, Computer-Assisted

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Artificial Intelligence-based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection.

Journal of pediatric gastroenterology and nutrition
OBJECTIVES: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical ima...

A Deep-Learning-Based, Fully Automated Program to Segment and Quantify Major Spinal Components on Axial Lumbar Spine Magnetic Resonance Images.

Physical therapy
OBJECTIVE: The paraspinal muscles have been extensively studied on axial lumbar magnetic resonance imaging (MRI) for better understanding of back pain; however, the acquisition of measurements mainly relies on manual segmentation, which is time consu...

A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study.

Investigative radiology
MATERIALS AND METHODS: Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneu...

COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning.

Studies in health technology and informatics
Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 infection in CT scans using deep convolutional neural networks. Furthermore, we evaluate the performa...

Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs.

Studies in health technology and informatics
In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image...

Double-path parallel convolutional neural network for removing speckle noise in different types of OCT images.

Applied optics
Speckle noises widely exist in optical coherence tomography (OCT) images. We propose an improved double-path parallel convolutional neural network (called DPNet) to reduce speckles. We increase the network width to replace the network depth to extrac...

Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis.

Stem cell reports
Lineage tracing is a powerful tool in developmental biology to interrogate the evolution of tissue formation, but the dense, three-dimensional nature of tissue limits the assembly of individual cell trajectories into complete reconstructions of devel...

Fluorescence microscopy datasets for training deep neural networks.

GigaScience
BACKGROUND: Fluorescence microscopy is an important technique in many areas of biological research. Two factors that limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging and, when ima...

Generative Adversarial Network Based Automatic Segmentation of Corneal Subbasal Nerves on In Vivo Confocal Microscopy Images.

Translational vision science & technology
PURPOSE: In vivo confocal microscopy (IVCM) is a noninvasive, reproducible, and inexpensive diagnostic tool for corneal diseases. However, widespread and effortless image acquisition in IVCM creates serious image analysis workloads on ophthalmologist...

[Application of deep learning assisted electronic laryngoscope in diagnosis of laryngeal leukoplakia].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery
In recent years, medical imaging technology and computer technology have made great progress. On the one hand, with the development and popularization of electronic laryngoscope, the image of electronic laryngoscope plays a very important role in the...