AIMC Topic: Tomography, X-Ray Computed

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Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

European radiology
OBJECTIVES: To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR).

A novel deep learning-based method for COVID-19 pneumonia detection from CT images.

BMC medical informatics and decision making
BACKGROUND: The sensitivity of RT-PCR in diagnosing COVID-19 is only 60-70%, and chest CT plays an indispensable role in the auxiliary diagnosis of COVID-19 pneumonia, but the results of CT imaging are highly dependent on professional radiologists.

Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy.

Nature communications
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it is the most time-consuming step as manual delineation is always required from radiation oncologists. Herein, we propose a ligh...

Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To investigate the performance of head-and-neck (HN) organs-at-risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep learning (DL) solutions.

The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.

BMC cancer
BACKGROUND: Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.

Deep-learning-based automatic facial bone segmentation using a two-dimensional U-Net.

International journal of oral and maxillofacial surgery
The use of deep learning (DL) in medical imaging is becoming increasingly widespread. Although DL has been used previously for the segmentation of facial bones in computed tomography (CT) images, there are few reports of segmentation involving multip...

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement o...

Benchmarking of Deep Architectures for Segmentation of Medical Images.

IEEE transactions on medical imaging
In recent years, there were many suggestions regarding modifications of the well-known U-Net architecture in order to improve its performance. The central motivation of this work is to provide a fair comparison of U-Net and its five extensions using ...

Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach.

Scientific reports
Early ischemic lesion on non-contrast computed tomogram (NCCT) in acute stroke can be subtle and need confirmation with magnetic resonance (MR) image for treatment decision-making. We retrospectively included the NCCT slices of 129 normal subjects an...

Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and re...