SIAM: Spatial and Intensity Awareness Module for cerebrovascular segmentation.

Background And Objectives: Cerebrovascular segmentation plays a crucial role in guiding the diagnosis and treatment of cerebrovascular diseases. With the rapid advancements in deep learning models, significant progress has been made in 3D cerebrovascular segmentation. However, they often rely on massive images and annotations, which is still challenging in cerebrovascular segmentation.

Multi-objective constraints for path planning in screw fixation of scaphoid fractures.

Purpose: Scaphoid fractures, a common type of clinical fracture, often require screw placement surgery to achieve optimal therapeutic outcomes. Path planning algorithms can avoid more risks and have vital potential for developing precise and automatic surgeries. Despite the success of surgical path planning algorithms, automatic path planning for scaphoid fractures remains challenging owing to the complex bone structure and individual variations.

Design and application of pneumatic rehabilitation glove system based on brain-computer interface.

Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments.

Linear semantic transformation for semi-supervised medical image segmentation.

Medical image segmentation is a focus research and foundation in developing intelligent medical systems. Recently, deep learning for medical image segmentation has become a standard process and succeeded significantly, promoting the development of reconstruction, and surgical planning of disease diagnosis. However, semantic learning is often inefficient owing to the lack of supervision of feature maps, resulting in that high-quality segmentation models always rely on numerous and accurate data annotations.

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning.

Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the skin conductance-based APs and non-APs recognition with machine learning, which could assist in APs detection and localization in clinical practice. Firstly, we collect skin conductance of traditional Five-Shu Point and their corresponding non-APs with wearable sensors, establishing a dataset containing over 36000 samples of 12 different AP types.

All answers are in the images: A review of deep learning for cerebrovascular segmentation.

Cerebrovascular imaging is a common examination. Its accurate cerebrovascular segmentation become an important auxiliary method for the diagnosis and treatment of cerebrovascular diseases, which has received extensive attention from researchers. Deep learning is a heuristic method that encourages researchers to derive answers from the images by driving datasets.

A learnable Gabor Convolution kernel for vessel segmentation.

Vessel segmentation is significant for characterizing vascular diseases, receiving wide attention of researchers. The common vessel segmentation methods are mainly based on convolutional neural networks (CNNs), which have excellent feature learning capabilities. Owing to inability to predict learning direction, CNNs generate large channels or sufficient depth to obtain sufficient features.

Pairwise attention-enhanced adversarial model for automatic bone segmentation in CT images.

. Bone segmentation is a critical step in screw placement navigation. Although the deep learning methods have promoted the rapid development for bone segmentation, the local bone separation is still challenging due to irregular shapes and similar representational features.

FDE-net: Frequency-domain enhancement network using dynamic-scale dilated convolution for thyroid nodule segmentation.

Thyroid nodules, a common disease of endocrine system, have a probability of nearly 10% to turn into malignant nodules and thus pose a serious threat to health. Automatic segmentation of thyroid nodules is of great importance for clinicopathological diagnosis. This work proposes FDE-Net, a combined segmental frequency domain enhancement and dynamic scale cavity convolutional network for thyroid nodule segmentation.

Generative Consistency for Semi-Supervised Cerebrovascular Segmentation From TOF-MRA.

Cerebrovascular segmentation from Time-of-flight magnetic resonance angiography (TOF-MRA) is a critical step in computer-aided diagnosis. In recent years, deep learning models have proved its powerful feature extraction for cerebrovascular segmentation. However, they require many labeled datasets to implement effective driving, which are expensive and professional.

Cerebrovascular segmentation in phase-contrast magnetic resonance angiography by multi-feature fusion and vessel completion.

Phase-Contrast Magnetic Resonance Angiography (PC-MRA) is a potential way of cerebrovascular imaging, which can suppress non-vascular tissue while presenting vessels. But PC-MRA will bring much noise and is easy to result in partially broken vessels. Usually, deep learning is an effective way to quantify vessels.

TMSF-Net: Multi-series fusion network with treeconnect for colorectal tumor segmentation.

Purpose: Colorectal tumors are common clinical diseases. Automatic segmentation of colorectal tumors captured in computed tomography (CT) images can provide numerous possibilities for computer-assisted treatment. Obtaining large datasets is expensive, and completing labeling is time- and manpower-consuming.

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.

(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics.

CSR-Net: Cross-Scale Residual Network for multi-objective scaphoid fracture segmentation.

The scaphoid is located in the carpals. Owing to the body structure and location of the scaphoid, scaphoid fractures are common and it is difficult to heal. Three-dimensional reconstruction of scaphoid fracture can accurately display the fracture surface and provide important support for the surgical plan involving screw placement.

Cerebrovascular segmentation from TOF-MRA based on multiple-U-net with focal loss function.

Background And Objective: Accurate cerebrovascular segmentation plays an important role in the diagnosis of cerebrovascular diseases. Considering the complexity and uncertainty of doctors' manual segmentation of cerebral vessels, this paper proposed an automatic segmentation algorithm based on Multiple-U-net (M-U-net) to segment cerebral vessel structures from the Time-of-flight Magnetic Resonance Angiography (TOF-MRA) data.

Methods: First, the TOF-MRA data was normalized by volume and then divided into three groups through slices of axial, coronal and sagittal directions respectively.

An Effective Deep Neural Network for Lung Lesions Segmentation From COVID-19 CT Images.

Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy to satisfy the applicability of 3-D network and remove irrelevant background.

Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory.

Auditory brainstem response (ABR) testing is an invasive electrophysiological auditory function test. Its waveforms and threshold can reflect auditory functional changes in the auditory centers in the brainstem and are widely used in the clinic to diagnose dysfunction in hearing. However, identifying its waveforms and threshold is mainly dependent on manual recognition by experimental persons, which could be primarily influenced by individual experiences.

Pathological lung segmentation in chest CT images based on improved random walker.

Background And Objective: Pathological lung segmentation as a pretreatment step in the diagnosis of lung diseases has been widely explored. Because of the complexity of pathological lung structures and gray blur of the border, accurate lung segmentation in clinical 3D computed tomography images is a challenging task. In view of the current situation, the work proposes a fast and accurate pathological lung segmentation method.

Segmentation of Cerebrovascular Anatomy from TOF-MRA Using Length-Strained Enhancement and Random Walker.

Cerebrovascular rupture can cause a severe stroke. Three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) is a common method of obtaining vascular information. This work proposes a fully automated segmentation method for extracting the vascular anatomy from TOF-MRA.

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer.

The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature.

Erratum to radiographic assessment of the cup orientation after total hip arthroplasty: a literature review.

[This corrects the article DOI: 10.21037/atm.2019.

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications.

In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation.

Radiographic assessment of the cup orientation after total hip arthroplasty: a literature review.

Optimal acetabular cup orientation is of substantial importance to good long-term function and low complication rates after total hip arthroplasty (THA). The radiographic anteversion (RA) and inclination (RI) angles of the cup are typically studied due to the practicability, simplicity, and ease of interpretation of their measurements. A great number of methods have been developed to date, most of which have been performed on pelvic or hip anteroposterior radiographs.

A novel method to model hepatic vascular network using vessel segmentation, thinning, and completion.

The accurate modeling of the liver vessel network structure is an important prerequisite for developing a preoperative plan for the liver. Considering that extracting liver blood vessels from patient's abdominal computed tomography(CT) images requires several manual operations, this study proposed an automatic segmentation method of liver vessels based on graph cut, thinning, and vascular combination, which can obtain a complete liver vascular network. First, the CT image was preprocessed by grayscale mapping based on sigmoid function, vessel enhancement based on Hessian filter, and denoising based on anisotropic filter to enhance the grayscale contrast between the vascular and non-vascular parts of the liver.