AIMC Topic: Image Processing, Computer-Assisted

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A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.

Brain imaging and behavior
A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. This proposed method includes preprocessing, classification, and segmentation of the primary occu...

Assessment of Skin Toxicity in an in Vitro Reconstituted Human Epidermis Model Using Deep Learning.

The American journal of pathology
Skin toxicity is a common safety concern associated with drugs that inhibit epidermal growth factor receptors as well as other targets involved in epidermal growth and differentiation. Recently, the use of a three-dimensional reconstructed human epid...

Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning.

Ultrasound in medicine & biology
Placenta localization from obstetric 2-D ultrasound (US) imaging is unattainable for many pregnant women in low-income countries because of a severe shortage of trained sonographers. To address this problem, we present a method to automatically detec...

Deep learning-based convolutional neural network for intramodality brain MRI synthesis.

Journal of applied clinical medical physics
PURPOSE: The existence of multicontrast magnetic resonance (MR) images increases the level of clinical information available for the diagnosis and treatment of brain cancer patients. However, acquiring the complete set of multicontrast MR images is n...

Colon tissue image segmentation with MWSI-NET.

Medical & biological engineering & computing
Developments in deep learning have resulted in computer-aided diagnosis for many types of cancer. Previously, pathologists manually performed the labeling work in the analysis of colon tissues, which is both time-consuming and labor-intensive. Result...

Quantification of uptake in pelvis F-18 FLT PET-CT images using a 3D localization and segmentation CNN.

Medical physics
PURPOSE: The purpose of this work was to develop and validate a deep convolutional neural network (CNN) approach for the automated pelvis segmentation in computed tomography (CT) scans to enable the quantification of active pelvic bone marrow by mean...

Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness.

Medical physics
PURPOSE: Deep learning (DL) is rapidly finding applications in low-dose CT image denoising. While having the potential to improve the image quality (IQ) over the filtered back projection method (FBP) and produce images quickly, performance generaliza...

Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks.

Sensors (Basel, Switzerland)
High-resolution digital elevation models (DEMs) play a critical role in geospatial databases, which can be applied to many terrain-related studies such as facility siting, hydrological analysis, and urban design. However, due to the limitation of pre...

Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features.

BMC medical imaging
BACKGROUND: Automated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA)...

Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network.

International journal of legal medicine
In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain insufficient as a result of lower accuracy due to the overlapping of pelvic organs in X-ray images. Segme...