A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation

Segmentation of the prostate boundary on clinical images is useful in a large number of applications including calculation of prostate volume pre- and post-treatment, to detect extra-capsular spread, and for creating patient-specific anatomical models. Manual segmentation of the prostate boundary is, however, time consuming and subject to inter- and intra-reader variability. T2-weighted (T2-w) magnetic resonance (MR) structural imaging (MRI) and MR spectroscopy (MRS) have recently emerged as promising modalities for detection of prostate cancer in vivo. MRS data consists of spectral signals measuring relative metabolic concentrations, and the metavoxels near the prostate have distinct spectral signals from metavoxels outside the prostate. Active Shape Models (ASM's) have become very popular segmentation methods for biomedical imagery. However, ASMs require careful initialization and are extremely sensitive to model initialization. The primary contribution of this paper is a scheme to automatically initialize an ASM for prostate segmentation on endorectal in vivo multi-protocol MRI via automated identification of MR spectra that lie within the prostate. A replicated clustering scheme is employed to distinguish prostatic from extra-prostatic MR spectra in the midgland. The spatial locations of the prostate spectra so identified are used as the initial ROI for a 2D ASM. The midgland initializations are used to define a ROI that is then scaled in 3D to cover the base and apex of the prostate. A multi-feature ASM employing statistical texture features is then used to drive the edge detection instead of just image intensity information alone. Quantitative comparison with another recent ASM initialization method by Cosio showed that our scheme resulted in a superior average segmentation performance on a total of 388 2D MRI sections obtained from 32 3D endorectal in vivo patient studies. Initialization of a 2D ASM via our MRS-based clustering scheme resulted in an average overlap accuracy (true positive ratio) of 0.60, while the scheme of Cosio yielded a corresponding average accuracy of 0.56 over 388 2D MR image sections. During an ASM segmentation, using no initialization resulted in an overlap of 0.53, using the Cosio based methodology resulted in an overlap of 0.60, and using the MRS-based methodology resulted in an overlap of 0.67, with a paired Student's t-test indicating statistical significance to a high degree for all results. We also show that the final ASM segmentation result is highly correlated (as high as 0.90) to the initialization scheme.     

Fully Automatic Plaque Segmentation in 3-D Carotid Ultrasound Images

Automatic segmentation of the carotid plaques from ultrasound images has been shown to be an important task for monitoring progression and regression of carotid atherosclerosis. Considering the complex structure and heterogeneity of plaques, a fully automatic segmentation method based on media-adventitia and lumen-intima boundary priors is proposed. This method combines image intensity with structure information in both initialization and a level-set evolution process. Algorithm accuracy was examined on the common carotid artery part of 26 3-D carotid ultrasound images (34 plaques ranging in volume from 2.5 to 456 mm³) by comparing the results of our algorithm with manual segmentations of two experts. Evaluation results indicated that the algorithm yielded total plaque volume (TPV) differences of −5.3 ± 12.7 and −8.5 ± 13.8 mm³ and absolute TPV differences of 9.9 ± 9.5 and 11.8 ± 11.1 mm³. Moreover, high correlation coefficients in generating TPV (0.993 and 0.992) between algorithm results and both sets of manual results were obtained. The automatic method provides a reliable way to segment carotid plaque in 3-D ultrasound images and can be used in clinical practice to estimate plaque measurements for management of carotid atherosclerosis.     

Fully automatic segmentation of the Femur from 3D-CT images using primitive shape recognition and statistical shape models

Purpose

Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM.

Method

Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting.

Results

The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm.

Conclusion

The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.     

A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images

Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi-resolution framework. The model parameters are then modified with the prior knowledge of the optimization space to achieve optimal prostate segmentation. In contrast to traditional statistical models of shape and intensity priors, we use posterior probabilities of the prostate region determined from random forest classification to build our appearance model, initialize and propagate our model. Furthermore, multiple mean models derived from spectral clustering of combined shape and appearance parameters are applied in parallel to improve segmentation accuracies. The proposed method achieves mean Dice similarity coefficient value of 0.91 ± 0.09 for 126 images containing 40 images from the apex, 40 images from the base and 46 images from central regions in a leave-one-patient-out validation framework. The mean segmentation time of the procedure is 0.67 ± 0.02 s.     

Evaluation of image features and search strategies for segmentation of bone structures in radiographs using Active Shape Models

In this paper, we evaluate various image features and different search strategies for fitting Active Shape Models (ASM) to bone object boundaries in digitized radiographs. The original ASM method iteratively refines the pose and shape parameters of the point distribution model driving the ASM by a least squares fit of the shape to update the target points at the estimated object boundary position, as determined by a suitable object boundary criterion. We propose an improved search procedure that is more robust against outlier configurations in the boundary target points by requiring subsequent shape changes to be smooth, which is imposed by a smoothness constraint on the displacement of neighbouring target points at each iteration and implemented by a minimal cost path approach. We compare the original ASM search method and our improved search algorithm with a third method that does not rely on iteratively refined target point positions, but instead optimizes a global Bayesian objective function derived from statistical a priori contour shape and image models. Extensive validation of these methods on a database containing more than 400 images of the femur, humerus and calcaneus using the manual expert segmentation as ground truth shows that our minimal cost path method is the most robust. We also evaluate various measures for capturing local image appearance around each boundary point and conclude that the Mahalanobis distance applied to normalized image intensity profiles extracted normal to the shape is the most suitable criterion among the tested ones for guiding the ASM optimization.     

Registration-based segmentation of three-dimensional ultrasound images for quantitative measurement of fetal craniofacial structure

Segmentation of a fetal head from three-dimensional (3-D) ultrasound images is a critical step in the quantitative measurement of fetal craniofacial structure. However, two main issues complicate segmentation, including fuzzy boundaries and large variations in pose and shape among different ultrasound images. In this article, we propose a new registration-based method for automatically segmenting the fetal head from 3-D ultrasound images. The proposed method first detects the eyes based on Gabor features to identify the pose of the fetus image. Then, a reference model, which is constructed from a fetal phantom and contains prior knowledge of head shape, is aligned to the image via feature-based registration. Finally, 3-D snake deformation is utilized to improve the boundary fitness between the model and image. Four clinically useful parameters including inter-orbital diameter (IOD), bilateral orbital diameter (BOD), occipital frontal diameter (OFD) and bilateral parietal diameter (BPD) are measured based on the results of the eye detection and head segmentation. Ultrasound volumes from 11 subjects were used for validation of the method accuracy. Experimental results showed that the proposed method was able to overcome the aforementioned difficulties and achieve good agreement between automatic and manual measurements.     

Phase Grouping-Based Needle Segmentation in 3-D Trans-rectal Ultrasound-Guided Prostate Trans-perineal Therapy

A robust and efficient needle segmentation method used to localize and track the needle in 3-D trans-rectal ultrasound (TRUS)-guided prostate therapy is proposed. The algorithmic procedure begins by cropping the 3-D US image containing a needle; then all voxels in the cropped 3-D image are grouped into different line support regions (LSRs) based on the outer product of the adjacent voxels' gradient vector. Two different needle axis extraction methods in the candidate LSR are presented: least-squares fitting and 3-D randomized Hough transform. Subsequent local optimization refines the position of the needle axis. Finally, the needle endpoint is localized by finding an intensity drop along the needle axis. The proposed methods were validated with 3-D TRUS tissue-mimicking agar phantom images, chicken breast phantom images and patient images obtained during prostate cryotherapy. The results of the in vivo test indicate that our method can localize the needle accurately and robustly with a needle endpoint localization accuracy <1.43 mm and detection accuracy >84%, which are favorable for 3-D TRUS-guided prostate trans-perineal therapy.     

Endocardial boundary extraction in left ventricular echocardiographic images using fast and adaptive B-spline snake algorithm

Purpose  

A fast and robust algorithm was developed for automatic segmentation of the left ventricular endocardial boundary in echocardiographic images. The method was applied to calculate left ventricular volume and ejection fraction estimation.     

Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation

One major limiting factor that prevents the accurate delineation of human organs has been the presence of severe pathology and pathology affecting organ borders. Overcoming these limitations is exactly what we are concerned in this study. We propose an automatic method for accurate and robust pathological organ segmentation from CT images. The method is grounded in the active shape model (ASM) framework. It leverages techniques from low-rank and sparse decomposition (LRSD) theory to robustly recover a subspace from grossly corrupted data. We first present a population-specific LRSD-based shape prior model, called LRSD-SM, to handle non-Gaussian gross errors caused by weak and misleading appearance cues of large lesions, complex shape variations, and poor adaptation to the finer local details in a unified framework. For the shape model initialization, we introduce a method based on patient-specific LRSD-based probabilistic atlas (PA), called LRSD-PA, to deal with large errors in atlas-to-target registration and low likelihood of the target organ. Furthermore, to make our segmentation framework more efficient and robust against local minima, we develop a hierarchical ASM search strategy. Our method is tested on the SLIVER07 database for liver segmentation competition, and ranks 3rd in all the published state-of-the-art automatic methods. Our method is also evaluated on some pathological organs (pathological liver and right lung) from 95 clinical CT scans and its results are compared with the three closely related methods. The applicability of the proposed method to segmentation of the various pathological organs (including some highly severe cases) is demonstrated with good results on both quantitative and qualitative experimentation; our segmentation algorithm can delineate organ boundaries that reach a level of accuracy comparable with those of human raters.     

Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

Purpose  Accurate localization and contouring of prostate are crucial issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. We introduce a new method for automatic 3D segmentation of the prostate gland from magnetic resonance imaging (MRI) scans. Methods  A statistical shape model was used as an a priori knowledge, and gray levels distribution was modeled by fitting histogram modes with a Gaussian mixture. Markov fields were used to introduce contextual information regarding voxels’ neighborhoods. Final labeling optimization is based on Bayesian a posteriori classification, estimated with the iterative conditional mode algorithm. Results  We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 12 cases, including prostates with cancer and benign prostatic hypertrophy, the mean Hausdorff distance and overlap ratio were 9.94 mm and 0.83, respectively. Conclusion  This new automatic prostate MRI segmentation method produces satisfactory results, even at prostate’s base and apex. The method is computationally feasible and efficient.     

Atlas-based prostate segmentation using an hybrid registration

Purpose  This paper presents the preliminary results of a semi-automatic method for prostate segmentation of magnetic resonance images (MRI) which aims to be incorporated in a navigation system for prostate brachytherapy. Methods  The method is based on the registration of an anatomical atlas computed from a population of 18 MRI exams onto a patient image. An hybrid registration framework which couples an intensity-based registration with a robust point-matching algorithm is used for both atlas building and atlas registration. Results  The method has been validated on the same dataset that the one used to construct the atlas using the leave-one-out method. Results gives a mean error of 3.39 mm and a standard deviation of 1.95 mm with respect to expert segmentations. Conclusions  We think that this segmentation tool may be a very valuable help to the clinician for routine quantitative image exploitation.     

Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation

Segmentation of medical images can be achieved with the help of model-based algorithms. Reliable boundary detection is a crucial component to obtain robust and accurate segmentation results and to enable full automation. This is especially important if the anatomy being segmented is too variable to initialize a mean shape model such that all surface regions are close to the desired contours. Several boundary detection algorithms are widely used in the literature. Most use some trained image appearance model to characterize and detect the desired boundaries. Although parameters of the boundary detection can vary over the model surface and are trained on images, their performance (i.e., accuracy and reliability of boundary detection) can only be assessed as an integral part of the entire segmentation algorithm. In particular, assessment of boundary detection cannot be done locally and independently on model parameterization and internal energies controlling geometric model properties.In this paper, we propose a new method for the local assessment of boundary detection called Simulated Search. This method takes any boundary detection function and evaluates its performance for a single model landmark in terms of an estimated geometric boundary detection error. In consequence, boundary detection can be optimized per landmark during model training. We demonstrate the success of the method for cardiac image segmentation. In particular we show that the Simulated Search improves the capture range and the accuracy of the boundary detection compared to a traditional training scheme. We also illustrate how the Simulated Search can be used to identify suitable classes of features when addressing a new segmentation task. Finally, we show that the Simulated Search enables multi-modal heart segmentation using a single algorithmic framework. On computed tomography and magnetic resonance images, average segmentation errors (surface-to-surface distances) for the four chambers and the trunks of the large vessels are in the order of 0.8 mm. For 3D rotational X-ray angiography images of the left atrium and pulmonary veins, the average error is 1.3 mm. In all modalities, the locally optimized boundary detection enables fully automatic segmentation.     

A high-throughput active contour scheme for segmentation of histopathological imagery

In this paper a minimally interactive high-throughput system which employs a color gradient based active contour model for rapid and accurate segmentation of multiple target objects on very large images is presented. While geodesic active contours (GAC) have become very popular tools for image segmentation, they tend to be sensitive to model initialization. A second limitation of GAC models is that the edge detector function typically involves use of gray scale gradients; color images usually being converted to gray scale, prior to gradient computation. For color images, however, the gray scale gradient image results in broken edges and weak boundaries, since the other channels are not exploited in the gradient computation. To cope with these limitations, we present a new GAC model that is driven by an accurate and rapid object initialization scheme; hierarchical normalized cuts (HNCut). HNCut draws its strength from the integration of two powerful segmentation strategies—mean shift clustering and normalized cuts. HNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale, mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial contour for a GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel. We show that the color gradient based edge-detector function results in more prominent boundaries compared to the classical gray scale gradient based function. By integrating the HNCut initialization scheme with color gradient based GAC (CGAC), HNCut-CGAC embodies five unique and novel attributes: (1) efficiency in segmenting multiple target structures; (2) the ability to segment multiple objects from very large images; (3) minimal human interaction; (4) accuracy; and (5) reproducibility. A quantitative and qualitative comparison of the HNCut-CGAC model against other state of the art active contour schemes (including a Hybrid Active Contour model (Paragios–Deriche) and a region-based AC model (Rousson–Deriche)), across 196 digitized prostate histopathology images, suggests that HNCut-CGAC is able to outperform state of the art hybrid and region based AC techniques. Our results show that HNCut-CGAC is computationally efficient and may be easily applied to a variety of different problems and applications.     

Prostate boundary detection and volume estimation using TRUS images for brachytherapy applications

Objective: Authors propose a semi-automatic segmentation algorithm for three-dimensional prostate boundary detection from trans-rectal ultrasound images. As a part of brachytherapy treatment with seeds for early stage prostate cancer, a patient’s prostate is scanned using a trans-rectal ultrasound probe, its boundary is manually outlined, and its volume is estimated for dosimetry purposes. Proposed algorithm requires a reduced amount of radiologist’s input, and thus speeds up the surgical procedure. Methods: The proposed segmentation algorithm utilizes texture differences between ultrasound images of the prostate and the surrounding tissues. It is carried out in the polar coordinate system and uses three-dimensional data correlation to improve the smoothness and reliability of the segmentation. The algorithm is applied to axial trans-rectal ultrasound images and the results are compared to the “ground truth” set by manual prostate boundary outlining (by experienced radiologist). Method is validated on six patients. Results: In our tests, the proposed algorithm estimated prostate volume within 95% of the original radiologist’s estimate. Conclusions: The boundary segmentation obtained from the algorithm can reduce manual input by a factor of 3, without significantly affecting the accuracy of the segmentation. The reduction in the manual input reduces the overall brachytherapy procedure time.     

Liver segmentation by intensity analysis and anatomical information in multi-slice CT images

Purpose  Quantitative assessment and essentially segmentation of liver and its tumours are of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Moreover, segmentation of liver is the basis of further processing such as visualization, liver resection planning, and liver shape analysis. In this paper, we propose an algorithm to estimate an initial liver boundary. Methods  The proposed method consists of four steps as follows: first, we compute statistical parameters of liver’s intensity range, associated with a large cross-section of liver CT image, utilizing expectation maximization (EM) algorithm. Second, by automatic extraction of ribs and segmentation of the heart, we define a ROI to confine the liver region for the next operations. Third, we propose a double thresholding approach to divide the liver intensity range into two overlapping ranges. In this case, based on a decision table, we label an object as a liver candidate or disregard it from the rest of the procedures. Finally, we employ an anatomical based rule to finalize a candidate as a liver tissue. In this case, we propose a color-map transformation scheme to convert the liver gray images into color images. In this way, we attempt to visually differentiate the liver from its surrounding tissues. Results  We have evaluated the techniques in the presence of 14 randomly selected local datasets as well as all datasets from the MICCAI 2007 Grand Challenge workshop database. For the local datasets, the average overlap error and average volume difference were of values of 15.3 and 2.8%, respectively. In the case of the MICCAI datasets, the above values were estimated as 20.3 and −4.5%, respectively. Conclusion  The results reveal that the proposed technique is feasible to perform consistent initial liver borders. The boundary might be then employed in an ‘Active Contour’ algorithm to finalize the liver mask.     

Method for Carotid Artery 3-D Ultrasound Image Segmentation Based on CSWin Transformer

Precise segmentation of carotid artery (CA) structure is an important prerequisite for the medical assessment and detection of carotid plaques. For automatic segmentation of the media–adventitia boundary (MAB) and lumen–intima boundary (LIB) in 3-D ultrasound images of the CA, a U-shaped CSWin transformer (U-CSWT) is proposed. Both the encoder and decoder of the U-CSWT are composed of hierarchical CSWT modules, which can capture rich global context information in the 3-D image. Experiments were performed on a 3-D ultrasound image data set of the CA, and the results indicate that the U-CSWT performs better than other convolutional neural network (CNN)-based and CNN–transformer hybrid methods. The model yields Dice coefficients of 94.6 ± 3.0% and 90.8 ± 5.1% for the MAB and LIB in the common carotid artery (CCA) and 92.9 ± 4.9% and 89.6 ± 6.2% for MAB and LIB in the bifurcation, respectively. Our U-CSWT is expected to become an effective method for automatic segmentation of 3-D ultrasound images of CA.     

Automated Skin Segmentation in Ultrasonic Evaluation of Skin Toxicity in Breast Cancer Radiotherapy

Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and −3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic.     

Statistical shape and texture model of quadrature phase information for prostate segmentation

Purpose  

Prostate volume estimation from segmentation of transrectal ultrasound (TRUS) images aids in diagnosis and treatment of prostate hypertrophy and cancer. Computer-aided accurate and computationally efficient prostate segmentation in TRUS images is a challenging task, owing to low signal-to-noise ratio, speckle noise, calcifications, and heterogeneous intensity distribution in the prostate region.     

Ultrasound image segmentation using spectral clustering

Segmentation of ultrasound images is necessary in a variety of clinical applications, but the development of automatic techniques is still an open problem. Spectral clustering techniques have recently become popular for data and image analysis. In particular, image segmentation has been proposed via the normalized cut (NCut) criterion. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The adaptation of the NCut technique to ultrasound is described first. Segmentation is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. The success of the segmentation on these test cases warrants further research into NCut-based segmentation of ultrasound images.     

Near Real-Time Robust Non-rigid Registration of Volumetric Ultrasound Images for Neurosurgery

Ultrasound images are acquired before and after the resection of brain tumors to help the surgeon to localize the tumor and its extent and to minimize the amount of residual tumor after the resection. Because the brain undergoes large deformation between these two acquisitions, deformable image-based registration of these data sets is of substantial clinical importance. In this work, we present an algorithm for non-rigid registration of ultrasound images (RESOUND) that models the deformation with free-form cubic B-splines. We formulate a regularized cost function that uses normalized cross-correlation as the similarity metric. To optimize the cost function, we calculate its analytic derivative and use the stochastic gradient descent technique to achieve near real-time performance. We further propose a robust technique to minimize the effect of non-corresponding regions such as the resected tumor and possible hemorrhage in the post-resection image. Using manually labeled corresponding landmarks in the pre- and post-resection ultrasound volumes, we illustrate that our registration algorithm reduces the mean target registration error from an initial value of 3.7 to 1.5 mm. We also compare RESOUND with the previous work of Mercier et al. (2013) and illustrate that it has three important advantages: (i) it is fully automatic and does not require a manual segmentation of the tumor, (ii) it produces smaller registration errors and (iii) it is about 30 times faster. The clinical data set is available online on the BITE database website.