Pattern Classification for Ovarian Tumors by Integration of Radiomics and Deep Learning Features.

Background: Ovarian tumor is a common female genital tumor, among which malignant tumors have a poor prognosis. The survival rate of 70% of patients with ovarian cancer is less than 5 years, while benign ovarian tumor is better, so the early diagnosis of ovarian cancer is important for the treatment and prognosis of patients.

Objectives: Our aim is to establish a classification model for ovarian tumors.

One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation.

Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation. Despite its outstanding performance, however, this method leads to undesired system complexity and also ignores the correlation among the models.

[Comparison between approximate entropy and regional homogeneity for identification of irritable bowel syndrome based on functional magnetic resonance imaging].

Objective: To compare the effectiveness and sensitivity of entropy and regional homogeneity (ReHo) for identifying irritable bowel syndrome (IBS) based on functional magnetic resonance imaging (fMRI).

Methods: Voxel-based approximate entropy (ApEn) was calculated based on findings of resting fMRI of 54 patients with IBS and 54 healthy control subjects. Feature selection was performed using independent sample -test, and support vector machine was then used to classify and identify different groups.

Self-adaptive weighted level set evolution based on local intensity difference for parotid ducts segmentation.

Background: Parotid ducts (PDs) play an important role in the diagnosis and treatment of parotid lesions. Segmentation of PDs from Cone beam computed tomography (CBCT) images has a significant impact to the pathological analysis of the parotid gland. Although level set methods (LSMs) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing LSMs to precisely and self-adaptively segment PDs from parotid duct (PD) images with both noise, intensity inhomogeneity, and vague boundary.

Soft Tissue/Bone Decomposition of Conventional Chest Radiographs Using Nonparametric Image Priors.

Background And Objective: When radiologists diagnose lung diseases in chest radiography, they can miss some lung nodules overlapped with ribs or clavicles. Dual-energy subtraction (DES) imaging performs well because it can produce soft tissue images, in which the bone components in chest radiography were almost suppressed but the visibility of nodules and lung vessels was still maintained. However, most routinely available X-ray machines do not possess the DES function.

Hippocampus Segmentation Based on Iterative Local Linear Mapping With Representative and Local Structure-Preserved Feature Embedding.

Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer's disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a promising result. However, the PBMAS approach needs high computation cost due to registration and the segmentation accuracy is subject to the registration accuracy.

Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration.

Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images.

[A fast adaptive active contour model based on local gray difference for parotid duct].

Objective: To establish a fast adaptive active contour model based on local gray difference for parotid duct image segmentation.

Methods: On the basis of the LBF model, we added the mean difference of the local gray scale inside and outside the contour as the energy term of the driving evolution curve, and the local gray-scale variance difference was used to replace and as the control term of the energy parameter value. Two local similarity factors of different neighborhood sizes were introduced to correct the effects of image gray unevenness and boundary blur to improve the segmentation efficiency.

Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors.

Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization.

[A radiomics-based model for differentiation between benign and malignant gastrointestinal stromal tumors].

Objective: To establish a model for discrimination between benign and malignant gastrointestinal stromal tumors (GIST) by analyzing the texture features extracted from computed tomography (CT) images.

Methods: The CT datasets were collected from 110 patients with GIST (including 80 as the training cohort and 30 as the validation cohort). Feature set reduction was executed with the 0.

Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF).

In this paper, we present an original multiple atlases level set framework (MALSF) for automatic, accurate and robust thalamus segmentation in magnetic resonance images (MRI). The contributions of the MALSF method are twofold. First, the main technical contribution is a novel label fusion strategy in the level set framework.

Lung Field Segmentation in Chest Radiographs From Boundary Maps by a Structured Edge Detector.

Lung field segmentation in chest radiographs (CXRs) is an essential preprocessing step in automatically analyzing such images. We present a method for lung field segmentation that is built on a high-quality boundary map detected by an efficient modern boundary detector, namely a structured edge detector (SED). A SED is trained beforehand to detect lung boundaries in CXRs with manually outlined lung fields.

Hippocampus Segmentation Based on Local Linear Mapping.

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear.

Liver DCE-MRI Registration in Manifold Space Based on Robust Principal Component Analysis.

A technical challenge in the registration of dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging in the liver is intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, a manifold-based registration framework for liver DCE-MR time series is proposed.

Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain.

Suppression of bony structures in chest radiographs (CXRs) is potentially useful for radiologists and computer-aided diagnostic schemes. In this paper, we present an effective deep learning method for bone suppression in single conventional CXR using deep convolutional neural networks (ConvNets) as basic prediction units. The deep ConvNets were adapted to learn the mapping between the gradients of the CXRs and the corresponding bone images.

Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction.

Unlabelled: Attenuation correction is important for PET reconstruction. In PET/MR, MR intensities are not directly related to attenuation coefficients that are needed in PET imaging. The attenuation coefficient map can be derived from CT images.

[Medical image segmentation based on guided filtering and multi-atlas].

A novel medical automatic image segmentation strategy based on guided filtering and multi-atlas is proposed to achieve accurate, smooth, robust, and reliable segmentation. This framework consists of 4 elements: the multi-atlas registration, which uses the atlas prior information; the label fusion, in which the similarity measure of the registration is used as the weight to fuse the warped label; the guided filtering, which uses the local information of the target image to correct the registration errors; and the threshold approaches used to obtain the segment result. The experimental results showed part among the 15 brain MRI images used to segment the hippocampus region, the proposed method achieved a median Dice coefficient of 86% on the left hippocampus and 87.

[Brain image segmentation based on multi-weighted probabilistic atlas].

We propose a multi-weighted probabilistic atlas to obtain accurate, robust, and reliable segmentation. The local similarity measure is used as the weight to compute the probabilistic atlas, and the distance field is used as the weight to incorporate the locality information of the atlas; the self-similarity is used as the weight to incorporate the local information of target image to refine the probabilistic atlas. Experimental results with brain MRI images showed that the proposed algorithm outperforms the common brain image segmentation methods and achieved a median Dice coefficient of 87.

Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information.

By using entropy and local neighborhood information, we present in this study a robust adaptive Gaussian regularizing Chan-Vese (CV) model to segment the myocardium from magnetic resonance images with intensity inhomogeneity. By utilizing the circular Hough transformation (CHT) our model is able to detect epicardial and endocardial contours of the left ventricle (LV) as circles automatically, and the circles are used as the initialization. In the cost functional of our model, the interior and exterior energies are weighted by the entropy to improve the robustness of the evolving curve.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents.

Automated interventricular septum segmentation for black-blood myocardial T2* measurement in thalassemia.

Purpose: To develop and validate an automated segmentation method that extracts the interventricular septum (IS) from myocardial black-blood images for the T2* measurement in thalassemia patients.

Materials And Methods: A total of 144 thalassemia major patients (age range, 11-51 years; 73 males) were scanned with a black-blood multi-echo gradient-echo sequence using a 1.5 Tesla Siemens Sonata system (flip angle 20°, sampling bandwidth 810 Hz/pixel, voxel size 1.

Intensity based image registration by minimizing exponential function weighted residual complexity.

In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results.

[An automatic subregion delineation method for T2 measurement of articular cartilage in the knee].

Objective: To propose a new method for automatic segmentation of manually determined knee articular cartilage into 9 subregions for T2 measurement.

Methods: The middle line and normal line were automatically obtained based on the outline of articular cartilage manually drawn by experienced radiologists. The region of articular cartilage was then equidistantly divided into 3 layers along the direction of the normal line, and each layer was further equidistantly divided into 3 segments along the direction of the middle line.

A robust medical image segmentation method using KL distance and local neighborhood information.

In this paper, we propose an improved Chan-Vese (CV) model that uses Kullback-Leibler (KL) distances and local neighborhood information (LNI). Due to the effects of heterogeneity and complex constructions, the performance of level set segmentation is subject to confounding by the presence of nearby structures of similar intensity, preventing it from discerning the exact boundary of the object. Moreover, the CV model cannot usually obtain accurate results in medical image segmentation in cases of optimal configuration of controlling parameters, which requires substantial manual intervention.

Retrieval of brain tumors with region-specific bag-of-visual-words representations in contrast-enhanced MRI images.

A content-based image retrieval (CBIR) system is proposed for the retrieval of T1-weighted contrast-enhanced MRI (CE-MRI) images of brain tumors. In this CBIR system, spatial information in the bag-of-visual-words model and domain knowledge on the brain tumor images are considered for the representation of brain tumor images. A similarity metric is learned through a distance metric learning algorithm to reduce the gap between the visual features and the semantic concepts in an image.