Anterior segment optical coherence tomography (AS-OCT) is a fundamental ophthalmic imaging technique. AS-OCT images can be examined by experts and segmented to provide quantitative metrics that inform clinical decision making. Manual segmentation of ...
Journal of visualized experiments : JoVE
Nov 30, 2022
Recently, deep learning-based segmentation models have been widely applied in the ophthalmic field. This study presents the complete process of constructing an orbital computed tomography (CT) segmentation model based on U-Net. For supervised learnin...
The early sign detection of liver lesions plays an extremely important role in preventing, diagnosing, and treating liver diseases. In fact, radiologists mainly consider Hounsfield Units to locate liver lesions. However, most studies focus on the ana...
Recently, researchers have introduced Transformer into medical image segmentation networks to encode long-range dependency, which makes up for the deficiencies of convolutional neural networks (CNNs) in global context modeling, and thus improves segm...
BACKGROUND: Deep artificial neural networks such as convolutional neural networks (CNNs) have been shown to be effective models for reducing noise in CT images while preserving anatomic details. A practical bottleneck for developing CNN-based denoisi...
OBJECTIVE: We examined a modified encoder-decoder architecture-based fully convolutional neural network, OrganNet, for simultaneous auto-segmentation of 24 organs at risk (OARs) in the head and neck, followed by validation tests and evaluation of cli...
BACKGROUND: Contouring of internal gross target volume (iGTV) is an essential part of treatment planning in radiotherapy to mitigate the impact of intra-fractional target motion. However, it is usually time-consuming and easily subjected to intra-obs...
BACKGROUND: Histological feature representation is advantageous for computer aided diagnosis (CAD) and disease classification when using predictive techniques based on machine learning. Explicit feature representations in computer tissue models can a...
PURPOSE: Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric maps, from where MR images of that individual at other...
BACKGROUND: We reported the concept of patient-specific deep learning (DL) for real-time markerless tumor segmentation in image-guided radiotherapy (IGRT). The method was aimed to control the attention of convolutional neural networks (CNNs) by artif...
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