AIMC Topic: Abdomen

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A Platform Integrating Acquisition, Reconstruction, Visualization, and Manipulator Control Modules for MRI-Guided Interventions.

Journal of digital imaging
This work presents a platform that integrates a customized MRI data acquisition scheme with reconstruction and three-dimensional (3D) visualization modules along with a module for controlling an MRI-compatible robotic device to facilitate the perform...

Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we ...

Machine learning for medical ultrasound: status, methods, and future opportunities.

Abdominal radiology (New York)
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving t...

Automated Radiology Report Summarization Using an Open-Source Natural Language Processing Pipeline.

Journal of digital imaging
Diagnostic radiologists are expected to review and assimilate findings from prior studies when constructing their overall assessment of the current study. Radiology information systems facilitate this process by presenting the radiologist with a subs...

Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports.

Journal of digital imaging
A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in th...

Intravoxel Incoherent Motion: Model-Free Determination of Tissue Type in Abdominal Organs Using Machine Learning.

Investigative radiology
PURPOSE: For diffusion data sets including low and high b-values, the intravoxel incoherent motion model is commonly applied to characterize tissue. The aim of the present study was to show that machine learning allows a model-free approach to determ...

Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

Journal of digital imaging
The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized int...

Automatic anatomy recognition in whole-body PET/CT images.

Medical physics
PURPOSE: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is...