AIMC Topic: Image Processing, Computer-Assisted

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Smart Pothole Detection Using Deep Learning Based on Dilated Convolution.

Sensors (Basel, Switzerland)
Roads make a huge contribution to the economy and act as a platform for transportation. Potholes in roads are one of the major concerns in transportation infrastructure. A lot of research has proposed using computer vision techniques to automate poth...

Human Segmentation and Tracking Survey on Masks for MADS Dataset.

Sensors (Basel, Switzerland)
Human segmentation and tracking often use the outcome of person detection in the video. Thus, the results of segmentation and tracking depend heavily on human detection results in the video. With the advent of Convolutional Neural Networks (CNNs), th...

Progressive attention module for segmentation of volumetric medical images.

Medical physics
PURPOSE: Medical image segmentation is critical for many medical image analysis applications. 3D convolutional neural networks (CNNs) have been widely adopted in the segmentation of volumetric medical images. The recent development of channelwise and...

Spine Medical Image Segmentation Based on Deep Learning.

Journal of healthcare engineering
The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image s...

MBANet: Multi-branch aware network for kidney ultrasound images segmentation.

Computers in biology and medicine
Due to the influence of kidney morphology, heterogeneous structure and image quality, segmenting kidney in ultrasound images is challenging. To alleviate this challenge, we proposed a novel deep neural network architecture, namely Multi-branch Aware ...

Training a deep learning model for single-cell segmentation without manual annotation.

Scientific reports
Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy i...

Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art for most medical image segmentation tasks. Issues with class imbalanc...

Deciphering tumour tissue organization by 3D electron microscopy and machine learning.

Communications biology
Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organizatio...

Automatic Robot-Driven 3D Reconstruction System for Chronic Wounds.

Sensors (Basel, Switzerland)
Chronic wounds, or wounds that are not healing properly, are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chron...