AIMC Topic: Tomography, X-Ray Computed

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Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support.

JAMA network open
IMPORTANCE: Pulmonary embolism (PE) is a life-threatening clinical problem, and computed tomographic imaging is the standard for diagnosis. Clinical decision support rules based on PE risk-scoring models have been developed to compute pretest probabi...

Learning to Quantify Emphysema Extent: What Labels Do We Need?

IEEE journal of biomedical and health informatics
Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variabi...

Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN.

Contrast media & molecular imaging
With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial ...

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images,...

Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.

Clinical radiology
AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas.

Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to report procedures developed to annotate abdominal computed tomography (CT) images from subjects without pancreatic disease that will be used as the input for deep convolutional neural networks (DNN) for devel...

Self-supervised learning for medical image analysis using image context restoration.

Medical image analysis
Machine learning, particularly deep learning has boosted medical image analysis over the past years. Training a good model based on deep learning requires large amount of labelled data. However, it is often difficult to obtain a sufficient number of ...

Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume.

International journal of computer assisted radiology and surgery
PURPOSE: Hydrocephalus is a clinically significant condition which can have devastating consequences if left untreated. Currently available methods for quantifying this condition using CT imaging are unreliable and prone to error. The purpose of this...

Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging.

Medical physics
PURPOSE: The improved soft tissue contrast of magnetic resonance imagingĀ (MRI) compared to computed tomography (CT) makes it a useful imaging modality for radiotherapy treatment planning. Even when MR images are acquired for treatment planning, the s...

Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans.

IEEE transactions on medical imaging
We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.g., pancreatic cyst), from abdominal CT scans. This is a challenging task in two aspects. First, some organs (e.g., the pancreas), are highl...