AIMC Topic: Abdomen

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Applications of Artificial Intelligence in Acute Abdominal Imaging.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of ...

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

Medical physics
BACKGROUND: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is us...

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective.

Sensors (Basel, Switzerland)
Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling p...

Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images.

Scientific reports
A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and post-contrast images. The algorithm was trained using dual-energy CT (DECT)-obtained portal venous p...

MOTC: Abdominal Multi-objective Segmentation Model with Parallel Fusion of Global and Local Information.

Journal of imaging informatics in medicine
Convolutional Neural Networks have been widely applied in medical image segmentation. However, the existence of local inductive bias in convolutional operations restricts the modeling of long-term dependencies. The introduction of Transformer enables...

Improving abdominal image segmentation with overcomplete shape priors.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The extraction of abdominal structures using deep learning has recently experienced a widespread interest in medical image analysis. Automatic abdominal organ and vessel segmentation is highly desirable to guide clinicians in computer-assisted diagno...

Interactive content-based image retrieval with deep learning for CT abdominal organ recognition.

Physics in medicine and biology
Recognizing the most relevant seven organs in an abdominal computed tomography (CT) slice requires sophisticated knowledge. This study proposed automatically extracting relevant features and applying them in a content-based image retrieval (CBIR) sys...

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections.

Abdominal radiology (New York)
PURPOSE: Abdominal ultrasound screening requires the capture of multiple standardized plane views as per clinical guidelines. Currently, the extent of adherence to such guidelines is dependent entirely on the skills of the sonographer. The use of neu...