AIMC Topic: Image Processing, Computer-Assisted

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Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.

Korean journal of radiology
OBJECTIVE: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction met...

Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning.

Tomography (Ann Arbor, Mich.)
A murine model of myelofibrosis in tibia was used in a co-clinical trial to evaluate segmentation methods for application of image-based biomarkers to assess disease status. The dataset (32 mice with 157 3D MRI scans including 49 test-retest pairs sc...

A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography.

Sensors (Basel, Switzerland)
In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diag...

Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images.

Computers in biology and medicine
Despite being widely utilized to help endoscopists identify gastrointestinal (GI) tract diseases using classification and segmentation, models based on convolutional neural network (CNN) have difficulties in distinguishing the similarities among some...

Comparison of Deep-Learning Image Reconstruction With Hybrid Iterative Reconstruction for Evaluating Lung Nodules With High-Resolution Computed Tomography.

Journal of computer assisted tomography
OBJECTIVE: This study aimed to investigate the impact of deep-learning reconstruction (DLR) on the detailed evaluation of solitary lung nodule using high-resolution computed tomography (HRCT) compared with hybrid iterative reconstruction (hybrid IR).

iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction.

Nature protocols
The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites...

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning.

Journal of visualized experiments : JoVE
The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inherent challenges in the segmentation of these small and morphologically diverse structures. In this ...

Formulation and Characterization of Buccal Films Containing Valsartan with Additional Support from Image Analysis.

AAPS PharmSciTech
The present study was aimed to the development and characterization of valsartan-containing buccal films with an introduction to a novel technique of image analysis. Visual inspection of the film provided a wealth of information that was difficult to...

Deep learning for automated segmentation of the temporomandibular joint.

Journal of dentistry
OBJECTIVE: Quantitative analysis of the volume and shape of the temporomandibular joint (TMJ) using cone-beam computed tomography (CBCT) requires accurate segmentation of the mandibular condyles and the glenoid fossae. This study aimed to develop and...

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

IEEE transactions on medical imaging
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically releva...