Moving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping.

BACKGROUND AND OBJECTIVES: Surgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Tracking of the instruments (graspers, needle drivers) and field of view from the stereoscopic camera during surgery could further help the surgeons to remain focussed and reduce the probability of committing any mistakes. Tracking is usually preferred in computerized video surveillance, traffic monitoring, military surveillance system, and vehicle navigation. Despite the numerous efforts over the last few years, object tracking still remains an open research problem, mainly due to motion blur, image noise, lack of image texture, and occlusion. Most of the existing object tracking methods are time-consuming and less accurate when the input video contains high volume of information and more number of instruments. METHODS:This paper presents a variational framework to track the motion of moving objects in surgery videos. The key contributions are as follows: (1) A denoising method using stochastic resonance in maximal overlap discrete wavelet transform is proposed and (2) a robust energy functional based on Bhattacharyya coefficient to match the target region in the first frame of the input sequence with the subsequent frames using a similarity metric is developed. A modified affine transformation-based registration is used to estimate the motion of the features following an active contour-based segmentation method to converge the contour resulted from the registration process. RESULTS AND CONCLUSION:The proposed method has been implemented on publicly available databases; the results are found satisfactory. Overlap index (OI) is used to evaluate the tracking performance, and the maximum OI is found to be 76% and 88% on private data and public data sequences

Heterogeneous System-on-Chip based Lattice-Boltzmann Visual Simulation System

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, that causes a localised dilation or ballooning of the blood vessel. It is life-threatening, hence an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel lattice-Boltzmann simulation software which is designed to provide the radiologist with estimates of flow rates, pressures and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualise the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this paper, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low cost embedded platform and to visualise the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second

HEMELB Acceleration and Visualization for Cerebral Aneurysms

A weakness in the wall of a cerebral artery causing a dilation or ballooning of the blood vessel is known as a cerebral aneurysm. Optimal treatment requires fast and accurate diagnosis of the aneurysm. HemeLB is a fluid dynamics solver for complex geometries developed to provide neurosurgeons with information related to the flow of blood in and around aneurysms. On a cost efficient platform, HemeLB could be employed in hospitals to provide surgeons with the simulation results in real-time. In this work, we developed an improved version of HemeLB for GPU implementation and result visualization. A visualization platform for smooth interaction with end users is also presented. Finally, a comprehensive evaluation of this implementation is reported. The results demonstrate that the proposed implementation achieves a maximum performance of 15,168,964 site updates per second, and is capable of speeding up HemeLB for deployment in hospitals and clinical investigations

Lattice-Boltzmann interactive blood flow simulation pipeline.

PURPOSE:Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering. METHODS:We have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores. RESULTS:The proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second. CONCLUSION:A reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information

Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip

Cerebral aneurysm is a weakness in a blood vessel that may enlarge and bleed into the surrounding area, which is a life-threatening condition. Therefore, early and accurate diagnosis of aneurysm is highly required to help doctors to decide the right treatment. This work aims to implement a real-time automated segmentation technique for cerebral aneurysm on the Zynq system-on-chip (SoC), and virtualize the results on a 3D plane, utilizing virtual reality (VR) facilities, such as Oculus Rift, to create an interactive environment for training purposes. The segmentation algorithm is designed based on hard thresholding and Haar wavelet transformation. The system is tested on six subjects, for each consists 512 × 512 DICOM slices, of 16 bits 3D rotational angiography. The quantitative and subjective evaluation show that the segmented masks and 3D generated volumes have admitted results. In addition, the hardware implement results show that the proposed implementation is capable to process an image using Zynq SoC in an average time of 5.2 ms

A lightweight neural network with multiscale feature enhancement for liver CT segmentation

Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.This publication was made possible by NPRP-11S-1219-170106 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work, and are solely the responsibility of the authors

CT Data for Upper Limb Prostheses and Lifelike Robotic Arms

The datasets contain high-resolution computed tomography (CT) reconstructed volume data from a female subject who had her left arm amputated and the prosthetic arm that was constructed. The first set of CT data consists of the uninjured right arm of the patient. The second set of data consists of the patient’s left arm with amputation below the elbow. The third set of data contains the CT data of the constructed prosthetic arm and socket. The data were the basis of the design, fabrication, and accuracy validation of prosthetic devices that were accomplished without direct physical measurement of the patients’ injured and uninjured parts of their limbs

Graphene-filled PDMS Composite for Tactile Sensing of Surgical Graspers

For tactile sensors to become useful technology, the required features should be flexibility, durability, and its sensitivity to physical contact. Conductive elastomer nanocomposites are widely used in fabricating a variety of electronic devices due to their excellent dispersion of the conductive nanomaterials. One such example is graphene in an elastomer matrix. In this study, we fabricated the transparent, flexible, and conductive force-responsive films from reduced graphene oxide (rGO)-filled polydimethylsiloxane (PDMS) elastomer composite. We used a simple yet unique way of mixing solution for composite preparation, which will enable an improved dispersion of filler in the matrix. Various characterization techniques were employed (i.e. SEM, FESEM, TEM, AFM XRD, UV visible spectroscopy, Raman studies, and impedance studies) to study the properties associated with the prepared thin film. The rGO was found to be well-dispersed in PDMS and it was found to behave appropriately as the sensing element during the capacitive force responsive mechanism in a metallic tip of surgical grasper. We anticipate that this kind of composites can find suitable applications for tactile sensing of surgical graspers.This work is supported by an NPRP grant from the Qatar National Research Fund under the grant no. NPRP 7-673-2-251. The statements made herein are solely the responsibility of the authors.Scopu

A method towards cerebral aneurysm detection in clinical settings

Cerebral aneurysms are among most prevalent and devastating cerebrovascular diseases of adult population worldwide. The resulting sequelae of untimely/inadequate therapeutic intervention include sub-arachnoid hemorrhage. Geometric modeling of aneurysm being the first step in the treatment planning, the scientists therefore focus more on segmentation of aneurysm rather than its detection. A successful aneurysm detection among the bunch of vessels would certainly facilitate and ease the segmentation process. In this work, we present a novel method for aneurysm detection; the key contributions are: contrast enhancement of input image using stochastic resonance concept in wavelet domain, adaptive thresholding, and modified Hough Circle Transform. Experimental results show that the proposed method is efficient in detecting the location and type of aneurysm.This work was partly supported by NPRP Grant #NPRP 5-792-2-328 from the Qatar National Research Fund (a member of the Qatar Foundation).Scopu

Experimental characterization of a tactile sensor for surgical applications

© 2018 IEEE. This paper presents an improved piezoelectric sensor fabricated using reduced graphene oxide (rGO)-filled polydimethylsiloxane (PDMS) elastomer composite that is able to sense the linear force applied onto its surface. The ultimate aim is to develop a haptic feedback interface for assisting surgeons in minimally invasive robotic surgery. A major challenge in robotic surgery systems is the lack of tactile feedback. Clinicians typically receive the visual information only about their surgical scene via the cameras. However, haptic feedback can improve the feedback information for clinicians and ultimately the surgical outcomes by aiding surgeons to differentiate between different tissue types as well as tactile feedback gives them the real feel of surgery (i.e. as performed with traditional open surgery). The results presented in this paper demonstrate that the sensor developed from graphene-filled PDMS can give robust and accurate force feedback that can be utilized as haptic feedback in further study. This paper illustrates two methods for characterizing the fabricated sensor in order to obtain force profile for a force range of 0.5 N-20 N. The main feature of the fabricated sensor is that it can be manufactured into any shape and size. It also gives compatibility for implementing the sensor externally over a robotic surgery solutions