Intelligent Evaluation Method for Scoliosis at Home Using Back Photos Captured by Mobile Phones.

The traditional scoliosis examination based on X-ray film is not suitable for large-scale screening, and it is also not suitable for dynamic evaluation during rehabilitation. Therefore, based on computer vision technology, this paper puts forward an evaluation method of scoliosis with different photos of the back taken by mobile phones, which involves three aspects: first, based on the key point detection model of YOLOv8, an algorithm for judging the type of spinal coronal curvature is proposed; second, an algorithm for evaluating the coronal plane of the spine based on the key points of the human back is proposed, aiming at quantifying the deviation degree of the spine in the coronal plane; third, the measurement algorithm of trunk rotation (ATR angle) based on multi-scale automatic peak detection (AMPD) is proposed, aiming at quantifying the deviation degree of the spine in sagittal plane. The public dataset and clinical paired data (mobile phone photo and X-ray) are used to test.

Assessment of Pelvic Tilt in Anteroposterior Radiographs by Area Ratio Based on Deep Learning.

Study Design: Diagnostics.

Objectives: Based on deep learning semantic segmentation model, we sought to assess pelvic tilt by area ratio of the lesser pelvic and the obturator foramen in anteroposterior (AP) radiographs.

Background: Pelvic tilt is a critical factor in hip and spinal surgery, commonly evaluated by medical professionals through sagittal pelvic radiographs.

A lightweight super-resolution network with skip-connections.

Introduction: In some hospitals in remote areas, due to the lack of MRI scanners with high magnetic field intensity, only low-resolution MRI images can be obtained, hindering doctors from making correct diagnoses. In our study, higher-resolution images were obtained through low-resolution MRI images. Moreover, as our algorithm is a lightweight algorithm with a small number of parameters, it can be carried out in remote areas under the condition of the lack of computing resources.

Cardiac Segmentation Method Based on Domain Knowledge.

Echocardiography plays an important role in the clinical diagnosis of cardiovascular diseases. Cardiac function assessment by echocardiography is a crucial process in daily cardiology. However, cardiac segmentation in echocardiography is a challenging task due to shadows and speckle noise.

Improved FCOS for Detecting Breast Cancers.

Purpose: Breast cancer ranks first among cancers affecting women's health. Our goal is to develop a fast, high-precision, and fully automated breast cancer detection algorithm to improve the early detection rate of breast cancer.

Methods: We compare different object detection algorithms, including anchor-based and anchor-free object detection algorithms for detecting breast lesions.

AI-assisted Method for Efficiently Generating Breast Ultrasound Screening Reports.

Background: Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report, which is time-consuming and laborious, and it is easy to miss and miswrite.

Aim: We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing.

Artificial Intelligence Medical Ultrasound Equipment: Application of Breast Lesions Detection.

Breast cancer ranks first among cancers affecting women's health. Our work aims to realize the intelligence of the medical ultrasound equipment with limited computational capability, which is used for the assistant detection of breast lesions. We embed the high-computational deep learning algorithm into the medical ultrasound equipment with limited computational capability by two techniques: (1) lightweight neural network: considering the limited computational capability of ultrasound equipment, a lightweight neural network is designed, which greatly reduces the amount of calculation.

Ultrasonic Diagnosis of Breast Nodules Using Modified Faster R-CNN.

Breast cancer has become the biggest threat to female health. Ultrasonic diagnosis of breast cancer based on artificial intelligence is basically a classification of benign and malignant tumors, which does not meet clinical demand. Besides, the current target detection method performs poorly in detecting small lesions, while it is clinically required to detect nodules below 2 mm.

Accurate and rapid CT image segmentation of the eyes and surrounding organs for precise radiotherapy.

Objective: The precise segmentation of organs at risk (OARs) is of importance for improving therapeutic outcomes and reducing injuries of patients undergoing radiotherapy. In this study, we developed a new approach for accurate computed tomography (CT) image segmentation of the eyes and surrounding organs, which is first locating then segmentation (FLTS).

Methods: The FLTS approach was composed of two steps: (a) classification of CT images using convolutional neural networks (CNN), and (b) segmentation of the eyes and surrounding organs using modified U-shape networks.

[The basic structure of heavy-ion tumor therapy facility].

Heavy-ions have the similar characteristic of depth-dose distribution with protons, but exhibit enhanced physical and radiobiological benefits. With increasing development in technical and clinical research, more facilities are being installed in the world. At the same time, many critical techniques of heavy-ion therapy facility were optimized and completed.

An adaptive window-setting scheme for segmentation of bladder tumor surface via MR cystography.

This paper proposes an adaptive window-setting scheme for noninvasive detection and segmentation of bladder tumor surface in T(1)-weighted magnetic resonance (MR) images. The inner border of the bladder wall is first covered by a group of ball-shaped detecting windows with different radii. By extracting the candidate tumor windows and excluding the false positive (FP) candidates, the entire bladder tumor surface is detected and segmented by the remaining windows.

A statistical study of the factors influencing the extent of respiratory motion blur in PET imaging.

Respiratory motion results in significant motion blur in thoracic and abdomen PET imaging. The extent of respiratory motion blur is mainly correlated with breathing amplitude, tumor size and location. In this paper we introduce a statistical study to quantitatively show the factors influencing the extent of respiratory motion blur in thoracic PET images.

Low-dose CT reconstruction via edge-preserving total variation regularization.

High radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with total variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low-contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method.

Respiratory motion blur identification and reduction in ungated thoracic PET imaging.

Respiratory motion results in significant motion blur in thoracic positron emission tomography (PET) imaging. Existing approaches to correct the blurring artifact involve acquiring the images in gated mode and using complicated reconstruction algorithms. In this paper, we propose a post-reconstruction framework to estimate respiratory motion and reduce the motion blur of PET images acquired in ungated mode.

Volume-based features for detection of bladder wall abnormal regions via MR cystography.

This paper proposes a framework for detecting the suspected abnormal region of the bladder wall via magnetic resonance (MR) cystography. Volume-based features are used. First, the bladder wall is divided into several layers, based on which a path from each voxel on the inner border to the outer border is found.

4D CT image simulation using B-Spline deformable model and cosine interpolation of deformation field.

Four-dimensional computed tomography(4D CT) is significant in radiotherapy treatment planning for thorax and upper abdomen to take their motion induced by respiration into consideration, but its high radiation dose becomes a major concern and impedes its wide application. To solve the problem, we propose an image interpolation approach to get 4D CT simulation images. We simulate 4D CT images at arbitrary intermediate phases by B-Spline deformable model with cosine interpolation of the deformation field, which is obtained by deformable registration of two CT images at end-exhale and end-inhale phases.

Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data.

Nonnegative Matrix Factorization (NMF) is a powerful tool for gene expression data analysis as it reduces thousands of genes to a few compact metagenes, especially in clustering gene expression samples for cancer class discovery. Enhancing sparseness of the factorisation can find only a few dominantly coexpressed metagenes and improve the clustering effectiveness. Sparse p-norm (p > 1) Nonnegative Matrix Factorization (Sp-NMF) is a more sparse representation method using high order norm to normalise the decomposed components.

On alpha-divergence based nonnegative matrix factorization for clustering cancer gene expression data.

Objective: Nonnegative matrix factorization (NMF) has been proven to be a powerful clustering method. Recently Cichocki and coauthors have proposed a family of new algorithms based on the alpha-divergence for NMF. However, it is an open problem to choose an optimal alpha.

Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis.

In microarray data analysis, each gene expression sample has thousands of genes and reducing such high dimensionality is useful for both visualization and further clustering of samples. Traditional principal component analysis (PCA) is a commonly used method which has problems. Nonnegative Matrix Factorization (NMF) is a new dimension reduction method.