AIMC Topic: Image Processing, Computer-Assisted

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Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

Frontiers in endocrinology
Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. (1) Develop...

Rapid reconstruction of highly undersampled, non-Cartesian real-time cine k-space data using a perceptual complex neural network (PCNN).

NMR in biomedicine
Highly accelerated real-time cine MRI using compressed sensing (CS) is a promising approach to achieve high spatio-temporal resolution and clinically acceptable image quality in patients with arrhythmia and/or dyspnea. However, its lengthy image reco...

Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
PURPOSE: The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.

Quantifying drug tissue biodistribution by integrating high content screening with deep-learning analysis.

Scientific reports
Quantitatively determining in vivo achievable drug concentrations in targeted organs of animal models and subsequent target engagement confirmation is a challenge to drug discovery and translation due to lack of bioassay technologies that can discrim...

Mammographic image classification with deep fusion learning.

Scientific reports
To better address the recognition of abnormalities among mammographic images, in this study we apply the deep fusion learning approach based on Pre-trained models to discover the discriminative patterns between Normal and Tumor categories. We designe...

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer.

Scientific reports
Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep ...

Estimation of Ultrasound Echogenicity Map from B-Mode Images Using Convolutional Neural Network.

Sensors (Basel, Switzerland)
In ultrasound B-mode imaging, speckle noises decrease the accuracy of estimation of tissue echogenicity of imaged targets from the amplitude of the echo signals. In addition, since the granular size of the speckle pattern is affected by the point spr...

Multi-level feature aggregation network for instrument identification of endoscopic images.

Physics in medicine and biology
Identification of surgical instruments is crucial in understanding surgical scenarios and providing an assistive process in endoscopic image-guided surgery. This study proposes a novel multilevel feature-aggregated deep convolutional neural network (...

Deep-Learning Generation of Synthetic Intermediate Projections Improves Lu SPECT Images Reconstructed with Sparsely Acquired Projections.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
The aims of this study were to decrease the Lu-SPECT acquisition time by reducing the number of projections and to circumvent image degradation by adding deep-learning-generated synthesized projections. We constructed a deep convolutional U-net-shap...