Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography.

Aim: To develop an automated model for subfoveal choroidal thickness (SFCT) detection in optical coherence tomography (OCT) images, addressing manual fovea location and choroidal contour challenges.

Methods: Two procedures were proposed: defining the fovea and segmenting the choroid. Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction (LocBscan-3D) predicted fovea location using central foveal depression features, and fovea localization from two-dimensional en-face OCT (LocEN-2D) used a mask region-based convolutional neural network (Mask R-CNN) model for optic disc detection, and determined the fovea location based on optic disc relative position.

Artificial intelligence-aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs.

Objective: The aim of this study was to develop an artificial intelligence-based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data.

Method: The transfer learning method was used to train a convolutional neural network (CNN) model with an external image dataset to extract the image features. Then, the last layer of the model was fine-tuned to determine the probability of ARDS.

Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model.

Propose: The proposed deep learning model with a mask region-based convolutional neural network (Mask R-CNN) can predict choroidal thickness automatically. Changes in choroidal thickness with age can be detected with manual measurements. In this study, we aimed to investigate choroidal thickness in a comprehensive aspect in healthy eyes by utilizing the Mask R-CNN model.

Application of Artificial Intelligence and Deep Learning for Choroid Segmentation in Myopia.

Purpose: To investigate the correlation between choroidal thickness and myopia progression using a deep learning method.

Methods: Two data sets, data set A and data set B, comprising of 123 optical coherence tomography (OCT) volumes, were collected to establish the model and verify its clinical utility. The proposed mask region-based convolutional neural network (R-CNN) model, trained with the pretrained weights from the Common Objects in Context database as well as the manually labeled OCT images from data set A, was used to automatically segment the choroid.

Automatic Segment and Quantify Choroid Layer in Myopic eyes: Deep Learning based Model.

Purpose: To report a rapid and accurate method based upon deep learning for automatic segmentation and measurement of the choroidal thickness (CT) in myopic eyes, and to determine the relationship between refractive error (RE) and CT.

Methods: Fifty-four healthy subjects 20-39 years of age were retrospectively reviewed. Data reviewed included age, gender, laterality, visual acuity, RE, and Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) images.

Multiview Contouring for Breast Tumor on Magnetic Resonance Imaging.

The shape and contour of the lesion are shown to be effective features for physicians to identify breast tumor as benign or malignant. The region of the lesion is usually manually created by the physician according to their clinical experience; therefore, contouring tumors on breast magnetic resonance imaging (MRI) is difficult and time-consuming. For this purpose, an automatic contouring method for breast tumors was developed for less burden in the analysis and to decrease the observed bias to help in making decisions clinically.

Mammographic Density Distribution of Healthy Taiwanese Women and its Naturally Decreasing Trend with Age.

We analysed typical mammographic density (MD) distributions of healthy Taiwanese women to augment existing knowledge, clarify cancer risks, and focus public health efforts. From January 2011 to December 2015, 88,193 digital mammograms were obtained from 69,330 healthy Taiwanese women (average, 1.27 mammograms each).

Using Flow Characteristics in Three-Dimensional Power Doppler Ultrasound Imaging to Predict Complete Responses in Patients Undergoing Neoadjuvant Chemotherapy.

Objectives: Strategies are needed for the identification of a poor response to treatment and determination of appropriate chemotherapy strategies for patients in the early stages of neoadjuvant chemotherapy for breast cancer. We hypothesize that power Doppler ultrasound imaging can provide useful information on predicting response to neoadjuvant chemotherapy.

Methods: The solid directional flow of vessels in breast tumors was used as a marker of pathologic complete responses (pCR) in patients undergoing neoadjuvant chemotherapy.

Effectiveness of evaluating tumor vascularization using 3D power Doppler ultrasound with high-definition flow technology in the prediction of the response to neoadjuvant chemotherapy for T2 breast cancer: a preliminary report.

The aim of this study was to evaluate the effectiveness of advanced ultrasound (US) imaging of vascular flow and morphological features in the prediction of a pathologic complete response (pCR) and a partial response (PR) to neoadjuvant chemotherapy for T2 breast cancer.Twenty-nine consecutive patients with T2 breast cancer treated with six courses of anthracycline-based neoadjuvant chemotherapy were enrolled. Three-dimensional (3D) power Doppler US with high-definition flow (HDF) technology was used to investigate the blood flow in and morphological features of the tumors.

Stellate masses and histologic grades in breast cancer.

Breast masses with a radiologic stellate pattern often transform into malignancies, but their tendency to be of low histologic grade yields a better survival rate compared with tumors with other patterns on mammography screening. This study was designed to investigate the correlation of histologic grade with stellate features extracted from the coronal plane of 3-D ultrasound images. A pre-processing method was proposed to facilitate the extraction of stellate features.

Three-dimensional region-based segmentation for breast tumors on sonography.

Because malignant and benign breast tumors show different shapes and sizes on sonography, information about tumor shapes and sizes is important for clinical diagnosis. Since sonograms include noise and tissue texture, accurate clinical diagnosis is highly dependent on clinical experience and expertise. However, manually sketching a 3-dimensional (3D) breast tumor contour is a time-consuming and complicated task.

Vascular morphologic information of three-dimensional power Doppler ultrasound is valuable in the classification of breast lesions.

Doppler ultrasound imaging provides vascular information that could characterize benign and malignant breast masses in many previous publications. In this study, we applied vascular quantification and morphology features derived from three-dimensional power Doppler ultrasound as classifiers based on support vector machine. An Az value under the receiver operating characteristic (ROC) curve was used to measure the significance of each vascularization feature.

Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography.

Rationale And Objectives: Advanced ischemic heart disease is usually accompanied by left ventricular (LV) myocardial volume loss and an abnormal enhancing pattern on delayed phase of multi-detector row computed tomography (MDCT). To assist radiologists and physicians in estimating the LV myocardial volume on delayed phase, this paper proposes an adaptive segmentation method for contouring the myocardial region in the delayed-phase MDCT and for computing the volume.

Materials And Methods: The proposed method uses an anisotropic diffusion filter as a preprocessing procedure to enhance contrast and reduce specks in MDCT imaging.

Left ventricular myocardium segmentation on arterial phase of multi-detector row computed tomography.

Rationale And Objectives: Variation of left ventricular myocardial volumes correlates closely with ischemic heart diseases. In clinical practice, because physicians and radiologists rely much on myocardial contour to diagnose many different cardiac diseases, automatic segmentation of left ventricular myocardium and quantifying myocardium characteristics is clinically beneficial. This paper presents a hybrid segmentation method for left ventricular myocardium on arterial phase of multi-detector row computed tomography (MDCT) imaging.

Computer-aided diagnosis with textural features for breast lesions in sonograms.

Rationale And Objectives: Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images.

Materials And Methods: The ultrasound (US) dataset evaluated in this study composed of 1020 sonograms of region of interest (ROI) subimages from 255 patients.

Comparative analysis of logistic regression, support vector machine and artificial neural network for the differential diagnosis of benign and malignant solid breast tumors by the use of three-dimensional power Doppler imaging.

Objective: Logistic regression analysis (LRA), Support Vector Machine (SVM) and a neural network (NN) are commonly used statistical models in computer-aided diagnostic (CAD) systems for breast ultrasonography (US). The aim of this study was to clarify the diagnostic ability of the use of these statistical models for future applications of CAD systems, such as three-dimensional (3D) power Doppler imaging, vascularity evaluation and the differentiation of a solid mass.

Materials And Methods: A database that contained 3D power Doppler imaging pairs of non-harmonic and tissue harmonic images for 97 benign and 86 malignant solid tumors was utilized.

Computer-aided diagnosis for breast tumors by using vascularization of 3-D power Doppler ultrasound.

This study assessed the accuracy of three-dimensional (3-D) power Doppler ultrasound in differentiating between benign and malignant breast tumors by using a support vector machine (SVM). A 3-D power Doppler ultrasonography was performed on 164 patients with 86 benign and 78 malignant breast tumors. The volume-of-interest (VOI) in 3-D ultrasound images was automatically generated from three rectangular regions-of-interest (ROI).

Left ventricular myocardial volumes measured during arterial and delayed phases of multidetector row computed tomography: a study on intra- and interobserver variability.

To compare and correlate left ventricular (LV) myocardial volumes obtained using arterial and delayed phases of multidetector row computed tomography (CT) and evaluate their intra- and interobserver variation. Two observers evaluated the arterial- and delayed-phase serial short-axis images of 45 healthy volunteers. Intra- and interobserver variations in LV myocardial volumes were correlated with four factors-myocardial volume, contrast-volume-to-body-weight ratio, and contrast-to-noise ratios in the arterial and delayed phases.

Characterization of benign and malignant solid breast masses: harmonic versus nonharmonic 3D power Doppler imaging.

The aim of this study was to compare the diagnostic performance of nonharmonic ultrasound (US) and tissue harmonic imaging (THI) using three-dimensional (3D) power Doppler sonographic technique to classify benign and malignant breast tumors by vascularization. From January 2003 to February 2004, we evaluated 200 patients and one of lobular carcinoma in situ was excluded from the malignant category. One hundred and ninety-nine subjects were enrolled.

Characterization of benign and malignant solid breast masses in harmonic 3D power Doppler imaging.

Purpose: The authors assessed the characteristics of benign and malignant solid breast tumors in harmonic three-dimensional (3D) power Doppler imaging and proposed decision models to classify benign and malignant breast tumors.

Materials And Methods: A total of 86 malignant and 97 benign harmonic 3D power Doppler US images were analyzed. All the harmonic 3D power Doppler images were obtained using a Voluson730 US system (GE, Zipf, Austria) equipped with a RSP 6-12 transducer and tissue harmonic imaging modalities.

Intra-tumor flow index can predict the malignant potential of breast tumor: dependent on age and volume.

The aim of this study was to assess tumor vascularity through three dimensional (3D) power Doppler breast ultrasound (US) and propose a decision model for the classification of benign and malignant breast tumors. Patient recruitment for this study was performed consecutively during a 13-mo period (January 2003 to February 2004). A total of 102 benign and 93 malignant solid breast images were analyzed.

Automatic contouring for breast tumors in 2-d sonography.

Automatic contouring for breast tumors using medical ultrasound (US) imaging may assist physicians, without relevant experience, in making correct diagnoses. This study utilizes the watershed transform and active contour model (ACM) to overcome the natural properties of US images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. The watershed transform is performed as the automatic initial contouring procedure to maintain a rough tumor shape and boundary.

Computer-Aided Diagnosis Applied to 3-D US of Solid Breast Nodules by Using Principal Component Analysis and Image Retrieval.

Textural features have been shown to be valuable in tumor diagnosis. This study combines three practical textural features in ultrasound (US) images, i.e.