MR-Net: Retinal OCTA Image Stitching via Multi-Scale Representation Learning and Dynamic Location Guidance.

Optical coherence tomography angiography (OCTA) plays a crucial role in quantifying and analyzing retinal vascular diseases. However, the limited field of view (FOV) inherent in most commercial OCTA imaging systems poses a significant challenge for clinicians, restricting the possibility to analyze larger retinal regions of high resolution. Automatic stitching of OCTA scans in adjacent regions may provide a promising solution to extend the region of interest.

COSTA: A Multi-center TOF-MRA Dataset and A Style Self-Consistency Network for Cerebrovascular Segmentation.

Time-of-flight magnetic resonance angiography (TOF-MRA) is the least invasive and ionizing radiation-free approach for cerebrovascular imaging, but variations in imaging artifacts across different clinical centers and imaging vendors result in inter-site and inter-vendor heterogeneity, making its accurate and robust cerebrovascular segmentation challenging. Moreover, the limited availability and quality of annotated data pose further challenges for segmentation methods to generalize well to unseen datasets. In this paper, we construct the largest and most diverse TOF-MRA dataset (COSTA) from 8 individual imaging centers, with all the volumes manually annotated.

Cross-modality transfer learning with knowledge infusion for diabetic retinopathy grading.

Background: Ultra-wide-field (UWF) fundus photography represents an emerging retinal imaging technique offering a broader field of view, thus enhancing its utility in screening and diagnosing various eye diseases, notably diabetic retinopathy (DR). However, the application of computer-aided diagnosis for DR using UWF images confronts two major challenges. The first challenge arises from the limited availability of labeled UWF data, making it daunting to train diagnostic models due to the high cost associated with manual annotation of medical images.

Robust and accurate corneal interfaces segmentation in 2D and 3D OCT images.

Segmentation of corneal layer interfaces in optical coherence tomography (OCT) images is important for diagnostic and surgical purposes, while manual segmentation is a time-consuming and tedious process. This paper presents a novel technique for the automatic segmentation of corneal layer interfaces using customized initial layer estimation and a gradient-based segmentation method. The proposed method was also extended to three-dimensional OCT images.

Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection.

Purpose: Fast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT).

Methods: Unlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture.

Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning.

Trigeminal neuralgia caused by paroxysmal and severe pain in the distribution of the trigeminal nerve is a rare chronic pain disorder. It is generally accepted that compression of the trigeminal root entry zone by vascular structures is the major cause of primary trigeminal neuralgia, and vascular decompression is the prior choice in neurosurgical treatment. Therefore, accurate preoperative modeling/segmentation/visualization of trigeminal nerve and its surrounding cerebrovascular is important to surgical planning.

Unbiased groupwise registration for shape prediction of foot scans.

A graph-based groupwise shape registration algorithm for building statistical shape model (SSM) is proposed, which has been successfully applied to shape prediction of foot scans. Establishing unbiased and effective shape correspondences of large-scale data sets is extremely challenging, for the inappropriate selection of initial mean shape and non-rigid registration of shape with large-scale deformation. To address these issues, first, we use a simplified graph to model the shape distribution in metric space and an edge-guided graph shrinkage to deform the shapes.

Cuff-less blood pressure measurement from dual-channel photoplethysmographic signals via peripheral pulse transit time with singular spectrum analysis.

Objective: This paper proposes an unobtrusive blood pressure (BP) measurement system design with a motion artifact (MA) compensation strategy as a potential surrogate to the traditional cuff-based sphygmomanometer for self-monitoring in a less restricted environment.

Approach: A dual-channel photoplethysmographic signal acquisition system is designed and implemented for cuff-less BP measurement based on the peripheral pulse transit time (PPTT) acquired from the forearm and wrist. Comprising a motion decision, singular spectrum analysis, PPTT calculation and BP measurement, a novel approach is proposed to realize BP measurements and suppress MA interference.

Registration and fusion quantification of augmented reality based nasal endoscopic surgery.

This paper quantifies the registration and fusion display errors of augmented reality-based nasal endoscopic surgery (ARNES). We comparatively investigated the spatial calibration process for front-end endoscopy and redefined the accuracy level of a calibrated endoscope by using a calibration tool with improved structural reliability. We also studied how registration accuracy was combined with the number and distribution of the deployed fiducial points (FPs) for positioning and the measured registration time.

Design and implementation of a BSN-based system for plantar health evaluation with exercise load quantification.

Background: Plantar pressure measurement has become increasingly useful in the evaluation of plantar health conditions thanks to the recent progression in sensing technology. Due to the large volume and high energy consumption of monitoring devices, traditional systems for plantar pressure measurement are only focused on static or short-term dynamic monitoring. It makes them inappropriate for early detections of plantar symptoms usually presented in long-term activities.

Observer-Based Non-PDC Control for Networked T-S Fuzzy Systems With an Event-Triggered Communication.

This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T-S fuzzy system are no longer needed in this paper.

Positive Unanimous Voting Algorithm for Focal Cortical Dysplasia Detection on Magnetic Resonance Image.

Focal cortical dysplasia (FCD) is the main cause of epilepsy and can be automatically detected via magnetic resonance (MR) images. However, visual detection of lesions is time consuming and highly dependent on the doctor's personal knowledge and experience. In this paper, we propose a new framework for positive unanimous voting (PUV) to detect FCD lesions.

Symbolic representation based on trend features for biomedical data classification.

Background: The widespread access to portable medical devices or new personal devices is boosting the amount of biomedical data. These devices provide a growing massive data that far exceeds the analytical ability of a professional doctor. The computer-assisted analysis of biomedical data has become an essential tool in medicine diagnosis.

A novel similarity comparison approach for dynamic ECG series.

The heart sound signal is a reflection of heart and vascular system motion. Long-term continuous electrocardiogram (ECG) contains important information which can be helpful to prevent heart failure. A single piece of a long-term ECG recording usually consists of more than one hundred thousand data points in length, making it difficult to derive hidden features that may be reflected through dynamic ECG monitoring, which is also very time-consuming to analyze.

Advances in biomedical engineering and biotechnology during 2013-2014.

The 3rd International Conference on Biomedical Engineering and Biotechnology (iCBEB 2014), held in Beijing from the 25th to the 28th of September 2014, is an annual conference that intends to provide an opportunity for researchers and practitioners around the world to present the most recent advances and future challenges in the fields of biomedical engineering, biomaterials, bioinformatics and computational biology, biomedical imaging and signal processing, biomechanical engineering and biotechnology, amongst others. The papers published in this issue are selected from this conference, which witnesses the advances in biomedical engineering and biotechnology during 2013-2014.

An ultrasonic transducer transient compensator design based on a simplified Variable Structure Control algorithm.

A non-linear control method, known as Variable Structure Control (VSC), is employed to reduce the duration of ultrasonic (US) transducer transients. A physically realizable system using a simplified form of the VSC algorithm is proposed for standard piezoelectric transducers and simulated. Results indicate a VSC-controlled transmitter reduces the transient duration to less than a carrier wave cycle.

High-brightness diode laser arrays integrated with a phase shifter designed for single-lobe far-field pattern.

High-brightness, edge-emitting diode laser arrays integrated with a phase shifter have been designed and fabricated at a wavelength of about 910 nm. Stable out-of-phase mode is generated through coupling evanescently and converted to be nearly in-phase by the phase modulation from the phase shifter. With a very simple manufacture process, stable single-lobe far-field pattern is achieved in the slow axis when the continuous wave output power exceeds 460 mW/facet, and the divergence angle is only 2.

Hybrid III-V/silicon single-mode laser with periodic microstructures.

In this Letter, a III-V/silicon hybrid single-mode laser operating at C band for photonic integration circuit is presented. The InGaAlAs gain structure is bonded onto a patterned silicon-on insulator through wafer to wafer directly. The mode selected mechanism based on a hybrid III-V/silicon straight cavity with periodic microstructures is applied, which only need low cost i-line projection photolithography in the whole technological process.

A hybrid silicon single mode laser with a slotted feedback structure.

In this paper, a III-V/Silicon hybrid single mode laser operating at a long wavelength for photonic integration circuit is presented. The InGaAlAs gain structure is bonded onto a patterned silicon-on insulator wafer directly. The novel mode selected mechanism based on a slotted silicon waveguide is applied, which only need standard photolithography in the whole technological process.