PixelTopoIS: a pixel-topology-coupled guidewire tip segmentation framework for robot-assisted intervention.

Purpose: Existing works showed great performance in pixel-level guidewire segmentation. However, topology-level segmentation has not been fully exploited in these works. Guidewire (tip) endpoint localization and (guidewire) loop detection are typical topology-level guidewire segmentation tasks.

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control.

Purpose: Radioactive seed implantation is an effective invasive treatment method for malignant liver tumors in hepatocellular carcinomas. However, challenges of the manual procedure may degrade the efficacy of the technique, such as the high accuracy requirement and radiation exposure to the surgeons. This paper aims to develop a robotic system and its control methods for assisting surgeons on the treatment.

A Sparse Volume Reconstruction Method for Fetal Brain MRI Using Adaptive Kernel Regression.

Slice-to-volume reconstruction (SVR) method can deal well with motion artifacts and provide high-quality 3D image data for fetal brain MRI. However, the problem of sparse sampling is not well addressed in the SVR method. In this paper, we mainly focus on the sparse volume reconstruction of fetal brain MRI from multiple stacks corrupted with motion artifacts.

A Novel Ultrasound Probe Spatial Calibration Method Using a Combined Phantom and Stylus.

Intra-operative ultrasound (US) is a popular imaging modality for its non-radiative and real-time advantages. However, it is still challenging to perform an interventional procedure under two-dimensional (2-D) US image guidance. Accordingly, the trend has been to perform three-dimensional (3-D) US image guidance by equipping the US probe with a spatial position tracking device, which requires accurate probe calibration for determining the spatial position between the B-scan image and the tracked probe.

MutScan: fast detection and visualization of target mutations by scanning FASTQ data.

Background: Some types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines.

Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images.

Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This framework includes several key components: pre-processing, contour initialization, and tumor segmentation.

Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling.

Background: Ultrasound imaging is safer than other imaging modalities, because it is noninvasive and nonradiative. Speckle noise degrades the quality of ultrasound images and has negative effects on visual perception and diagnostic operations.

Methods: In this paper, a nonlocal total variation (NLTV) method for ultrasonic speckle reduction is proposed.

GPU-based volume reconstruction for freehand 3D ultrasound imaging.

Volume reconstruction plays an important role in improving image quality for freehand three-dimensional (3D) ultrasound imaging. The kernel regression provides an effective method for volume reconstruction in 3D ultrasound imaging, but it requires heavily computational time-cost. In this paper, a programmable graphic-processor-unit-(GPU) based fast kernel regression method is proposed for freehand 3D ultrasound volume reconstruction.

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels.

Nonlocal Total-Variation-Based Speckle Filtering for Ultrasound Images.

Ultrasound is one of the most important medical imaging modalities for its real-time and portable imaging advantages. However, the contrast resolution and important details are degraded by the speckle in ultrasound images. Many speckle filtering methods have been developed, but they are suffered from several limitations, difficult to reach a balance between speckle reduction and edge preservation.

Reconstruction of freehand 3D ultrasound based on kernel regression.

Introduction: Freehand three-dimensional (3D) ultrasound has the advantages of flexibility for allowing clinicians to manipulate the ultrasound probe over the examined body surface with less constraint in comparison with other scanning protocols. Thus it is widely used in clinical diagnose and image-guided surgery. However, as the data scanning of freehand-style is subjective, the collected B-scan images are usually irregular and highly sparse.

Segmentation of abdomen MR images using kernel graph cuts with shape priors.

Background: Abdominal organs segmentation of magnetic resonance (MR) images is an important but challenging task in medical image processing. Especially for abdominal tissues or organs, such as liver and kidney, MR imaging is a very difficult task due to the fact that MR images are affected by intensity inhomogeneity, weak boundary, noise and the presence of similar objects close to each other.

Method: In this study, a novel method for tissue or organ segmentation in abdomen MR imaging is proposed; this method combines kernel graph cuts (KGC) with shape priors.

A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE.

Background: Computer-assisted surgical navigation aims to provide surgeons with anatomical target localization and critical structure observation, where medical image processing methods such as segmentation, registration and visualization play a critical role. Percutaneous renal intervention plays an important role in several minimally-invasive surgeries of kidney, such as Percutaneous Nephrolithotomy (PCNL) and Radio-Frequency Ablation (RFA) of kidney tumors, which refers to a surgical procedure where access to a target inside the kidney by a needle puncture of the skin. Thus, kidney segmentation is a key step in developing any ultrasound-based computer-aided diagnosis systems for percutaneous renal intervention.

An acceleration-based control framework for interactive gaming.

In this paper we presented a 3-D acceleration-based interactive framework using Body Sensor Networks (BSN) for real-time game controls. The framework consists of three modules: a wireless signal acquisition module that senses the accelerations of body movements, a signal processing module that uses the Kalman filter to rectify the contaminated acceleration data, and a control module that makes interactive gaming strategies. Our framework enables a wearable gaming control solution that differs from the conventional methods using joysticks, key boards or mice.