AIMC Topic: Tomography, X-Ray Computed

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Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography.

PloS one
A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extractio...

Knowledge-Aided Convolutional Neural Network for Small Organ Segmentation.

IEEE journal of biomedical and health informatics
Accurate and automatic organ segmentation is critical for computer-aided analysis towards clinical decision support and treatment planning. State-of-the-art approaches have achieved remarkable segmentation accuracy on large organs, such as the liver ...

Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning.

Physics in medicine and biology
Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could m...

MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning.

Physics in medicine and biology
Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based meth...

Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls.

Journal of forensic and legal medicine
A deep learning artificial neural network was adapted to the task of sex determination of skeletal remains. The neural network was trained on images of 900 skulls virtually reconstructed from hospital CT scans. When tested on previously unseen images...

Deep-learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography.

Medical physics
PURPOSE: We are developing a computerized segmentation tool for the inner and outer bladder wall as a part of an image analysis pipeline for CT urography (CTU).

Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset.

Biomedical engineering online
BACKGROUND: Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT ima...

Classification and Quantification of Emphysema Using a Multi-Scale Residual Network.

IEEE journal of biomedical and health informatics
Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted in this area, there still remain two major challenges. First, different emphysematous tissue appears ...

State-of-the-art review on deep learning in medical imaging.

Frontiers in bioscience (Landmark edition)
Deep learning (DL) is affecting each and every sphere of public and private lives and becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the activities of neurons in the neocortex of human brain where the thought...