AIMC Topic: Image Processing, Computer-Assisted

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Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR.

PloS one
The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a no...

Deep learning-based framework for automatic cranial defect reconstruction and implant modeling.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This article presents a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling.

Adaptive Multi-ROI Agricultural Robot Navigation Line Extraction Based on Image Semantic Segmentation.

Sensors (Basel, Switzerland)
Automated robots are an important part of realizing sustainable food production in smart agriculture. Agricultural robots require a powerful and precise navigation system to be able to perform tasks in the field. Aiming at the problems of complex ima...

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images.

Sensors (Basel, Switzerland)
Charts are often used for the graphical representation of tabular data. Due to their vast expansion in various fields, it is necessary to develop computer algorithms that can easily retrieve and process information from chart images in a helpful way....

Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images.

Oxidative medicine and cellular longevity
Ovarian cancer is one of the most common malignant tumours of female reproductive organs in the world. The pelvic CT scan is a common examination method used for the screening of ovarian cancer, which shows the advantages in safety, efficiency, and p...

A novel 3D lumbar vertebrae location and segmentation method based on the fusion envelope of 2D hybrid visual projection images.

Computers in biology and medicine
In recent years, fast and precise lumbar vertebrae segmentation technology have been one of the important topics in practical medical diagnosis and assisted medical surgery scenarios. However, most of the existing vertebral segmentation methods are b...

Fast and Efficient Image Novelty Detection Based on Mean-Shifts.

Sensors (Basel, Switzerland)
Image novelty detection is a repeating task in computer vision and describes the detection of anomalous images based on a training dataset consisting solely of normal reference data. It has been found that, in particular, neural networks are well-sui...

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks.

PloS one
This study proposes a neural network framework for modeling the foam effects found in liquid simulation without noise. The position and advection of the foam particles are calculated using the existing screen projection method, and the noise problem ...

Image quality improvement in low-dose chest CT with deep learning image reconstruction.

Journal of applied clinical medical physics
OBJECTIVES: To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm.

A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images.

Sensors (Basel, Switzerland)
Building segmentation is crucial for applications extending from map production to urban planning. Nowadays, it is still a challenge due to CNNs' inability to model global context and Transformers' high memory need. In this study, 10 CNN and Transfor...