Automated prostate recognition: a key process for clinically effective robotic prostatectomy

Clinical trials of PROBOT, a robotic system for prostate surgery, have shown that robotic surgery of soft tissue can be successful. Monitoring of the progress of the resection has shown to be a necessary feature of an effective robotic system for prostate surgery. It should provide the surgeon with a reliable method of assessing the cavity during resection. An automatic system for intraoperative monitoring of the progress of the resection during robotic prostatectomy consists of two subsystems: real-time intraoperative imaging of the prostate and automatic identification of the contour of the gland on each image. The development of a fully automatic scheme for prostate recognition on transurethral ultrasound scans is reported. A genetic algorithm has been developed to automatically adjust a model of the prostate boundary until an optimum fit to the prostate in a given image is obtained. An analysis of its performance on 22 different ultrasound images showed an average error of 6.21 mm. Use of a genetic algorithm and a constrained prostate model have shown to be a robust way to automatically identify the prostate in ultrasound images. The scheme is able to produce approximate prostate boundaries, without any human intervention, on ultrasound scans of varying quality. In addition to soft tissue robotic surgery, the genetic algorithm technique is also applicable to a wide range of computer assisted surgical techniques.     

The use of ultrasound in automated prostatectomy

A new way of applying transurethral ultrasound scanning in the common surgical procedure of the transurethral resection of the prostate is described. The scanning is incorporated as part of a robotic procedure for surgery, so that the overall time spent in an operation can be further shortened, and a safe and accurate operation can be achieved. The prostate dimensions obtained pre-operatively by the transrectal method and those obtained operatively are compared. A robotic system, which was developed specifically to remove prostatic adenoma automatically, is discussed. The system, called a motorised frame, is briefly described, together with its predecessor, a manual frame, in relation to ultrasound measurements. Sizing of the prostate pre-operatively using transrectal ultrasound methods is discussed, using both the manual and the motorised frame. The shortcomins of transrectal ultrasound for use in a robotic procedure are highlighted.     

Automatic detection of linear artifacts in medical images

Our purpose in this study is to describe an algorithm for the automatic detection of linear artifacts in medical images. Linear artifacts arise as a result of many different forms of tissues and tissue boundaries within the imaging volume. Additionally, linear artifacts can arise for artificial structures such as radioactive seeds and radioactive linear sources. It is the purpose of the described algorithm to automatically detect linear artifacts of a certain length and diameter. The algorithm was written and compiled on a Pentium-4 based computer in the Microsoft Visual C/C++ language. Inert coils supplied by Radiomed Inc. were implanted into a standard prostate ultrasound phantom. Transaxial ultrasound images of the implanted phantom were obtained at 2 mm increments. The coded algorithm was then applied to the ultrasound imaging volume to automatically segment out the implanted coils. Thirteen coils were implanted in the prostate phantom. Thirteen coils were automatically identified in the imaging volume. An algorithm was developed to automatically determine the position and orientation of radioactive coils within an imaging volume. The algorithm successfully identified thirteen coils implanted in an ultrasound prostate phantom.     

An Effective Non-rigid Registration Approach for Ultrasound Image Based On “Demons” Algorithm

Medical image registration is an important component of computer-aided diagnosis system in diagnostics, therapy planning, and guidance of surgery. Because of its low signal/noise ratio (SNR), ultrasound (US) image registration is a difficult task. In this paper, a fully automatic non-rigid image registration algorithm based on demons algorithm is proposed for registration of ultrasound images. In the proposed method, an “inertia force” derived from the local motion trend of pixels in a Moore neighborhood system is produced and integrated into optical flow equation to estimate the demons force, which is helpful to handle the speckle noise and preserve the geometric continuity of US images. In the experiment, a series of US images and several similarity measure metrics are utilized for evaluating the performance. The experimental results demonstrate that the proposed method can register ultrasound images efficiently, robust to noise, quickly and automatically.     

Varying ultrasound power level to distinguish surgical instruments and tissue

We investigate a new framework of surgical instrument detection based on power-varying ultrasound images with simple and efficient pixel-wise intensity processing. Without using complicated feature extraction methods, we identified the instrument with an estimated optimal power level and by comparing pixel values of varying transducer power level images. The proposed framework exploits the physics of ultrasound imaging system by varying the transducer power level to effectively distinguish metallic surgical instruments from tissue. This power-varying image-guidance is motivated from our observations that ultrasound imaging at different power levels exhibit different contrast enhancement capabilities between tissue and instruments in ultrasound-guided robotic beating-heart surgery. Using lower transducer power levels (ranging from 40 to 75% of the rated lowest ultrasound power levels of the two tested ultrasound scanners) can effectively suppress the strong imaging artifacts from metallic instruments and thus, can be utilized together with the images from normal transducer power levels to enhance the separability between instrument and tissue, improving intraoperative instrument tracking accuracy from the acquired noisy ultrasound volumetric images. We performed experiments in phantoms and ex vivo hearts in water tank environments. The proposed multi-level power-varying ultrasound imaging approach can identify robotic instruments of high acoustic impedance from low-signal-to-noise-ratio ultrasound images by power adjustments.     

Comparison of contrast in brightness mode and strain ultrasonography of glial brain tumours

ABSTRACT: BACKGROUND: Image contrast between normal tissue and brain tumours may sometimes appear to be low in intraoperative ultrasound. Ultrasound imaging of strain is an image modality that has been recently explored for intraoperative imaging of the brain. This study aims to investigate differences in image contrast between ultrasound brightness mode (B-mode) images and ultrasound strain magnitude images of brain tumours. METHODS: Ultrasound radiofrequency (RF) data was acquired during surgery in 15 patients with glial tumours. The data were subsequently processed to provide strain magnitude images. The contrast in the B-mode images and the strain images was determined in assumed normal brain tissue and tumour tissue at selected regions of interest (ROI). Three measurements of contrast were done in the ultrasound data for each patient. The B-mode and strain contrasts measurements were compared using the paired samples t- test. RESULTS: The statistical analysis of a total of 45 measurements shows that the contrasts in the strain magnitude images are significantly higher than in the conventional ultrasound B-mode images (P < 0.0001). CONCLUSIONS: The results indicate that ultrasound strain imaging provides better discrimination between normal brain tissue and glial tumour tissue than conventional ultrasound B- mode imaging. Ultrasound imaging of tissue strain therefore holds the potential of becoming a valuable adjunct to conventional intraoperative ultrasound imaging in brain tumour surgery.     

Outlining the prostate boundary using the harmonics method

In a computerised ultrasound image guidance for automated prostatectomy system, it is necessary to identify a smooth, continuous contour for the prostate (boundary) from the ultrasound image. The radial bas-relief (RBR) method, which has been reported previously, can extract a skeletonised image from an ultrasound image automatically. After this process the prostate boundary is clearly revealed. However, analysis of the image is far from complete, as there are many spurious branches that create too much ambiguity for the system to define the actual boundary. There are also sections missing from the prostate boundary. Therefore further post-processing is required to describe and define the prostate boundary. In the paper, the harmonics method is used to describe the prostate boundary. The harmonics method uses Fourier information for noise removal and encodes a smooth boundary. The results of using the harmonics method after application of the RBR method on ultrasound images are presented. Factors that affect the performance are also highlighted and discussed.     

Intraoperative ultrasound for guidance and tissue shift correction in image-guided neurosurgery

We present a surgical guidance system that incorporates pre-operative image information (e.g., MRI) with intraoperative ultrasound (US) imaging to detect and correct for brain tissue deformation during image-guided neurosurgery (IGNS). Many interactive IGNS implementations employ pre-operative images as a guide to the surgeons throughout the procedure. However, when a craniotomy is involved, tissue movement during a procedure can be a significant source of error in these systems. By incorporating intraoperative US imaging, the target volume can be scanned at any time, and two-dimensional US images may be compared directly to the corresponding slice from the pre-operative image. Homologous points may be mapped from the intraoperative to the pre-operative image space with an accuracy of better than 2 mm, enabling the surgeon to use this information to assess the accuracy of the guidance system along with the progress of the procedure (e.g., extent of lesion removal) at any time during the operation. Anatomical features may be identified on both the pre-operative and intraoperative images and used to generate a deformation map, which can be used to warp the pre-operative image to match the intraoperative US image. System validation is achieved using a deformable multi-modality imaging phantom, and preliminary clinical results are presented.     

Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery

An image-based re-registration scheme has been developed and evaluated that uses fiducial registration as a starting point to maximize the normalized mutual information (nMI) between intraoperative ultrasound (iUS) and preoperative magnetic resonance images (pMR). We show that this scheme significantly (p<0.001) reduces tumor boundary misalignment between iUS pre-durotomy and pMR from an average of 2.5 mm to 1.0 mm in six resection surgeries. The corrected tumor alignment before dural opening provides a more accurate reference for assessing subsequent intraoperative tumor displacement, which is important for brain shift compensation as surgery progresses. In addition, we report the translational and rotational capture ranges necessary for successful convergence of the nMI registration technique (5.9 mm and 5.2 deg, respectively). The proposed scheme is automatic, sufficiently robust, and computationally efficient (<2 min), and holds promise for routine clinical use in the operating room during image-guided neurosurgical procedures.     

基于超声图像的乳腺钙化点自动检测技术

当前乳腺钙化点检测方法多基于X光片,难以应用于超声图像,本研究提出基于超声图像的乳腺钙化点自动检测技术,首先将乳腺超声图像中的肿瘤区域通过勾画模板提取出来,基于简单线性迭代聚类算法进行超像素分割;然后提取表征各超像素的特征量来计算显著性图,基于钙化区域显著性进行粗钙化点分割;最终对分割后的粗钙化点进行形态学检测,达到对超声图像中的细钙化点自动检测。该方法取得了较好的分割效果,具有较强的鲁棒性,为形成具有普适性的肿瘤自动诊断方案奠定了研究基础。     

Application of soft tissue modelling to image-guided surgery

The deformation of soft tissue compromises the accuracy of image-guided surgery based on preoperative images, and restricts its applicability to surgery on or near bony structures. One way to overcome these limitations is to combine biomechanical models with sparse intraoperative data, in order to realistically warp the preoperative image to match the surgical situation. We detail the process of biomechanical modelling in the context of image-guided surgery. We focus in particular on the finite element method, which is shown to be a promising approach, and review the constitutive relationships which have been suggested for representing tissue during surgery. Appropriate intraoperative measurements are required to constrain the deformation, and we discuss the potential of the modalities which have been applied to this task. This technology is on the verge of transition into clinical practice, where it promises to increase the guidance accuracy and facilitate less invasive interventions. We describe here how soft tissue modelling techniques have been applied to image-guided surgery applications.     

Segmentation automatique d’échographies trans-abdominales de prostate pour le recalage d''images

This article presents a method for automatic segmentation of prostate from abdominal freehand ultrasound images. A statistical model of prostate is estimated from a manually delineated images. The segmentation starts by smoothing the image to enhance edges by applying a morphological and adaptive filter which detects individual speckles and remove them, while it preserves valuable details. Then the boundary is initialised starting from the model and the final form is estimated by a simulated annealing optimisation algorithm. The performances of the algorithm were compared with manual segmentation by an expert, the average distance was 3.7 pixels and an overlap surface of 93%.     

Prostate boundary segmentation from 2D ultrasound images

Outlining, or segmenting, the prostate is a very important task in the assignment of appropriate therapy and dose for cancer treatment; however, manual outlining is tedious and time-consuming. In this paper, an algorithm is described for semiautomatic segmentation of the prostate from 2D ultrasound images. The algorithm uses model-based initialization and the efficient discrete dynamic contour. Initialization requires the user to select only four points from which the outline of the prostate is estimated using cubic interpolation functions and shape information. The estimated contour is then deformed automatically to better fit the image. The algorithm can easily segment a wide range of prostate images, and contour editing tools are included to handle more difficult cases. The performance of the algorithm with a single user was compared to manual outlining by a single expert observer. The average distance between semiautomatically and manually outlined boundaries was found to be less than 5 pixels (0.63 mm), and the accuracy and sensitivity to area measurements were both over 90%.     

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 +/- 0.51 pixels (0.54 +/- 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.     

Automated localization of implanted seeds in 3D TRUS images used for prostate brachytherapy

An algorithm has been developed in this paper to localize implanted radioactive seeds in 3D ultrasound images for a dynamic intraoperative brachytherapy procedure. Segmentation of the seeds is difficult, due to their small size in relatively low quality of transrectal ultrasound (TRUS) images. In this paper, intraoperative seed segmentation in 3D TRUS images is achieved by performing a subtraction of the image before the needle has been inserted, and the image after the seeds have been implanted. The seeds are searched in a "local" space determined by the needle position and orientation information, which are obtained from a needle segmentation algorithm. To test this approach, 3D TRUS images of the agar and chicken tissue phantoms were obtained. Within these phantoms, dummy seeds were implanted. The seed locations determined by the seed segmentation algorithm were compared with those obtained from a volumetric cone-beam flat-panel micro-CT scanner and human observers. Evaluation of the algorithm showed that the rms error in determining the seed locations using the seed segmentation algorithm was 0.98 mm in agar phantoms and 1.02 mm in chicken phantoms.     

Estimation of bladder wall location in ultrasound images

A method of automatically estimating the location of the bladder wall in ultrasound images is proposed. Obtaining this estimate is intended to be the first stage in the development of an automatic bladder volume calculation system. The first step in the bladder wall estimation scheme involves globally processing the images using standard image processing techniques to highlight the bladder wall. Separate processing sequences are required to highlight the anterior bladder wall and the posterior bladder wall. The sequence to highlight the anterior bladder wall involves Gaussian smoothing and second differencing followed by zero-crossing detection. Median filtering followed by thresholding and gradient detection is used to highlight as much of the rest of the bladder wall as was visible in the original images. Then a ‘bladder wall follower’—a line follower with rules based on the characteristics of ultrasound imaging and the anatomy involved—is applied to the processed images to estimate the bladder wall location by following the portions of the bladder wall which are highlighted and filling in the missing segments. The results achieved using this scheme are presented.     

Line filtering for surgical tool localization in 3D ultrasound images

We present a method for automatic surgical tool localization in 3D ultrasound images based on line filtering, voxel classification and model fitting. This could possibly provide assistance for biopsy needle or micro-electrode insertion, or a robotic system performing this insertion. The line-filtering method is first used to enhance the contrast of the 3D ultrasound image, then a classifier is chosen to separate the tool voxels, in order to reduce the number of outliers. The last step is Random Sample Consensus (RANSAC) model fitting. Experimental results on several different polyvinyl alcohol (PVA) cryogel data sets demonstrate that the failure rate of the method proposed herein is improved by at least 86% compared to the model-fitting RANSAC algorithm with axis accuracy better than 1 mm, at the expense of only a modest increase in computational effort. The results of this experiment show that this system could be useful for clinical applications.     

神经元干细胞图像的全自动分割与追踪

基于显微细胞图像的全自动分割算法，建立了一种全自动追踪序列图像中的神经元干细胞的系统，序列图像的初始图结合了人机交互，干预分割结果。所有的细胞在追踪过程中将其分成惰性细胞，活跃细胞，分裂细胞和成串细胞。不同种类的细胞采用不同的追踪算法。一种特殊的后向追踪可以修改和纠正前向追踪里出现的错误，并以轨迹图的方式显示最后的追踪结果。     

一种快速的全自动超声子宫图像分割算法

使用超声成像进行子宫节育环检查工作已在我国广泛地开展,利用图像识别技术进行计算机辅助诊断对于减轻检查人员工作负担意义十分明显,其中图像分割部分的主要目标则是快速地全自动分割开图中的几个主要器官及节育环。本研究提出了一种快速的全自动子宫图像分割算法。该算法包括以下三个主要步骤:首先运用BP神经网络处理图像整体灰度分布获取基准分割阈值;其后使用超声图像斑点噪声统计特征进行同质区域判别,并根据局部灰度分布自适应调整分割阈值;最后使用数学形态学算子对分割效果做进一步的改善。基于由1200幅超声子宫图像组成的图像库,对所提算法与最大类别方差法、SNAKE活动轮廓模型等数种常用分割算法进行了性能比较,实验结果表明所提算法在速度与准确程度两方面均表现良好,平均耗时为0.93s/幅,准确程度达到了94%。本算法无需人工干预,分割速度快,分割准确程度能够被临床医生所接受,可以用作超声子宫图像辅助诊断系统的图像分割部分,具有很好的应用前景。