AIMC Topic: Image Processing, Computer-Assisted

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A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations.

Neural networks : the official journal of the International Neural Network Society
Deep Neural Networks are widely used for solving complex problems in several scientific areas, such as speech recognition, machine translation, image analysis. The strategies employed to investigate their theoretical properties mainly rely on Euclide...

Deep learning-based dynamic PET parametric K image generation from lung static PET.

European radiology
OBJECTIVES: PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric K provides better quantification and improve speci...

WaSR-A Water Segmentation and Refinement Maritime Obstacle Detection Network.

IEEE transactions on cybernetics
Obstacle detection using semantic segmentation has become an established approach in autonomous vehicles. However, existing segmentation methods, primarily developed for ground vehicles, are inadequate in an aquatic environment as they produce many f...

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.

Communications biology
Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a...

A deep learning generative model approach for image synthesis of plant leaves.

PloS one
OBJECTIVES: A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this s...

On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images.

Medical image analysis
Deep learning-based segmentation methods provide an effective and automated way for assessing the structure and function of the heart in cardiac magnetic resonance (CMR) images. However, despite their state-of-the-art performance on images acquired f...

Efficient Object Detection Based on Masking Semantic Segmentation Region for Lightweight Embedded Processors.

Sensors (Basel, Switzerland)
Because of the development of image processing using cameras and the subsequent development of artificial intelligence technology, various fields have begun to develop. However, it is difficult to implement an image processing algorithm that requires...

MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.

European radiology
OBJECTIVES: The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI.

Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks.

STAR protocols
Cellular calcium fluorescence imaging utilized to study cellular behaviors typically results in large datasets and a profound need for standardized and accurate analysis methods. Here, we describe open-source software (4SM) to overcome these limitati...