AIMC Topic: Image Processing, Computer-Assisted

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Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.

Methods in molecular biology (Clifton, N.J.)
Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, c...

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review.

Critical reviews in oncogenesis
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extractio...

X-ray image enhancement with multi-scale local edge preserving filter based on fuzzy entropy.

Journal of X-ray science and technology
BACKGROUND: Recently, X-rays have been widely used to detect complex structural workpieces. Due to the uneven thickness of the workpiece and the high dynamic range of the X-ray image itself, the detailed internal structure of the workpiece cannot be ...

A user-friendly deep learning application for accurate lung cancer diagnosis.

Journal of X-ray science and technology
BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faste...

Deep Learning-based Thigh Muscle Investigation Using MRI For Prosthetic Development for Patients Undergoing Total Knee Replacement (TKR).

Current medical imaging
BACKGROUND: A prosthetic device is designed based on the quantitative analysis of muscle MRI which will improve the muscle control achieved with functional electrical stimulation/ guided robotic exoskeletons. Electromyography (EMG) provides muscle fu...

The effect of the re-segmentation method on improving the performance of rectal cancer image segmentation models.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Rapid and accurate segmentation of tumor regions from rectal cancer images can better understand the patientâs lesions and surrounding tissues, providing more effective auxiliary diagnostic information. However, cutting rectal tumors with...

Deep learning approaches for breast cancer detection in histopathology images: A review.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in rese...

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.

Journal of X-ray science and technology
OBJECTIVES: To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive s...

Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images.

Journal of X-ray science and technology
In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient's condition is crucial in modern medicine since it determines whether or not the patie...

Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

Current medical imaging
BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually.