AIMC Topic: Image Processing, Computer-Assisted

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Impulsive Synchronization of Unbounded Delayed Inertial Neural Networks With Actuator Saturation and Sampled-Data Control and its Application to Image Encryption.

IEEE transactions on neural networks and learning systems
The article considers the impulsive synchronization for inertial neural networks with unbounded delay and actuator saturation via sampled-data control. Based on an impulsive differential inequality, the difficulties caused by unbounded delay and impu...

Design and independent training of composable and reusable neural modules.

Neural networks : the official journal of the International Neural Network Society
Monolithic neural networks and end-to-end training have become the dominating trend in the field of deep learning, but the steady increase in complexity and training costs has raised concerns about the effectiveness and efficiency of this approach. W...

Echocardiographic image multi-structure segmentation using Cardiac-SegNet.

Medical physics
PURPOSE: Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise o...

An end-to-end-trainable iterative network architecture for accelerated radial multi-coil 2D cine MR image reconstruction.

Medical physics
PURPOSE: Iterative convolutional neural networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reconstruction problems across different imaging modalities. However, beca...

Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction.

IEEE transactions on medical imaging
Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent sp...

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI.

IEEE transactions on medical imaging
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automati...

Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system.

PloS one
BACKGROUND: Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagnose on a plain radiograph. An automated way of classifying radiographic images could improve diagnos...

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages.

PloS one
The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with differ...

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning.

Journal of dental research
Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to se...

Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.

Radiology
Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose T...