AIMC Topic: Image Processing, Computer-Assisted

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3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nanometer range and strong contrast for membranous structures without requiring labeling or chemical fixation. T...

Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality.

Academic radiology
RATIONALE AND OBJECTIVES: Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations....

A single stage knowledge distillation network for brain tumor segmentation on limited MR image modalities.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Precisely segmenting brain tumors using multimodal Magnetic Resonance Imaging (MRI) is an essential task for early diagnosis, disease monitoring, and surgical planning. Unfortunately, the complete four image modalities utili...

Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning.

Neural networks : the official journal of the International Neural Network Society
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end,...

Sources of performance variability in deep learning-based polyp detection.

International journal of computer assisted radiology and surgery
PURPOSE: Validation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiatives aim to develop comprehensive theoretical frameworks for un...

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation.

Computers in biology and medicine
Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of ...

AMSUnet: A neural network using atrous multi-scale convolution for medical image segmentation.

Computers in biology and medicine
In recent years, Unet and its variants have gained astounding success in the realm of medical image processing. However, some Unet variant networks enhance their performance while increasing the number of parameters tremendously. For lightweight and ...

Cardiac phase detection in echocardiography using convolutional neural networks.

Scientific reports
Echocardiography is a commonly used and cost-effective test to assess heart conditions. During the test, cardiologists and technicians observe two cardiac phases-end-systolic (ES) and end-diastolic (ED)-which are critical for calculating heart chambe...

Context Label Learning: Improving Background Class Representations in Semantic Segmentation.

IEEE transactions on medical imaging
Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensi...

Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI.

IEEE transactions on medical imaging
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI) because it leads to patient discomfort and motion artifacts. Although several MRI techniques have been proposed to reduce the acquisition time, compressed sen...