AIMC Topic: Image Processing, Computer-Assisted

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Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with F-FDG,  Ga-DOTATATE, and F-Fluciclovine.

European journal of nuclear medicine and molecular imaging
UNLABELLED: A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using F-FDG,  Ga-DOTATATE, and F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps e...

Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Deep convolutional neural networks (CNNs) have yielded promising results in automatic whole slide images (WSIs) processing for digital pathology in recent years. Training supervised CNNs usually requires a large amount of annotated sample...

AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval.

Sensors (Basel, Switzerland)
Image retrieval techniques are becoming famous due to the vast availability of multimedia data. The present image retrieval system performs excellently on labeled data. However, often, data labeling becomes costly and sometimes impossible. Therefore,...

Reconstructing high fidelity digital rock images using deep convolutional neural networks.

Scientific reports
Imaging methods have broad applications in geosciences. Scanning electron microscopy (SEM) and micro-CT scanning have been applied for studying various geological problems. Despite significant advances in imaging capabilities, and image processing al...

Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.

Arthritis research & therapy
Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which c...

DeepUCT: Complex cascaded deep learning network for improved ultrasound tomography.

Physics in medicine and biology
Ultrasound computed tomography is an inexpensive and radiation-free medical imaging technique used to quantify the tissue acoustic properties for advanced clinical diagnosis. Image reconstruction in ultrasound tomography is often modeled as an optimi...

Automatic wound detection and size estimation using deep learning algorithms.

PLoS computational biology
Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of...

Deep Neural Networks for Medical Image Segmentation.

Journal of healthcare engineering
Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of th...

TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classification.

Neural networks : the official journal of the International Neural Network Society
Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is a challenging task due to a variety of issues, such as color inconsistency that results from the non-uniform manual staining operations, clustering of nuclei,...

Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.

Journal of applied clinical medical physics
PURPOSE: Adaptive radiotherapy requires auto-segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto-segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN can...