Optimized prostate biopsy via a statistical atlas of cancer spatial distribution

A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy

Background and purpose

Radiation therapy (RT) is commonly indicated for treatment of prostate cancer (PC). Biologicallyoptimised RT for PC may improve disease-free survival. This requires accurate spatial localisation and characterisation of tumour lesions. We aimed to generate a statistical, voxelised biological model to complement in vivomultiparametric MRI data to facilitate biologically-optimised RT.

Material and methods

Ex vivo prostate MRI and histopathological imaging were acquired for 63 PC patients. These data were co-registered to derive three-dimensional distributions of graded tumour lesions and cell density. Novel registration processes were used to map these data to a common reference geometry. Voxelised statistical models of tumour probability and cell density were generated to create the PC biological atlas. Cell density models were analysed using the Kullback-Leibler divergence to compare normal vs. lognormal approximations to empirical data.

Results

A reference geometry was constructed using ex vivo MRI space, patient data were deformably registered using a novel anatomy-guided process. Substructure correspondence was maintained using peripheral zone definitions to address spatial variability in prostate anatomy between patients. Three distinct approaches to interpolation were designed to map contours, tumour annotations and cell density maps from histology into ex vivo MRI space. Analysis suggests a log-normal model provides a more consistent representation of cell density when compared to a linear-normal model.

Conclusion

A biological model has been created that combines spatial distributions of tumour characteristics from a population into three-dimensional, voxelised, statistical models. This tool will be used to aid the development of biologically-optimised RT for PC patients.

Radiomics and radiogenomics of prostate cancer

Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer.

Medical Image Synthesis with Deep Convolutional Adversarial Networks

Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the network. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate

PURPOSE

We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer.

MATERIALS AND METHODS

In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space.

RESULTS

Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate.

CONCLUSION

We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis

In prostate cancer radiotherapy, computed tomography (CT) is widely used for dose planning purposes. However, because CT has low soft tissue contrast, it makes manual contouring difficult for major pelvic organs. In contrast, magnetic resonance imaging (MRI) provides high soft tissue contrast, which makes it ideal for accurate manual contouring. Therefore, the contouring accuracy on CT can be significantly improved if the contours in MRI can be mapped to CT domain by registering MRI with CT of the same subject, which would eventually lead to high treatment efficacy. In this paper, we propose a bi-directional image synthesis based approach for MRI-to-CT pelvic image registration. First, we use patch-wise random forest with auto-context model to learn the appearance mapping from CT to MRI domain, and then vice versa. Consequently, we can synthesize a pseudo-MRI whose anatomical structures are exactly same with CT but with MRI-like appearance, and a pseudo-CT as well. Then, our MRI-to-CT registration can be steered in a dual manner, by simultaneously estimating two deformation pathways: 1) one from the pseudo-CT to the actual CT and 2) another from actual MRI to the pseudo-MRI. Next, a dual-core deformation fusion framework is developed to iteratively and effectively combine these two registration pathways by using complementary information from both modalities. Experiments on a dataset with real pelvic CT and MRI have shown improved registration performance of the proposed method by comparing it to the conventional registration methods, thus indicating its high potential of translation to the routine radiation therapy.

Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models

PURPOSE

Accurate segmentation of pelvic organs in CT images is of great importance in external beam radiotherapy for prostate cancer. The aim of this studying is to develop a novel method for automatic, multiorgan segmentation of the male pelvis.

METHODS

The authors' segmentation method consists of several stages. First, a pretreatment includes parameterization, principal component analysis (PCA), and an established process of region-specific hierarchical appearance cluster (RSHAC) model which was executed on the training dataset. After the preprocessing, online automatic segmentation of new CT images is achieved by combining the RSHAC model with the PCA-based point distribution model. Fifty pelvic CT from eight prostate cancer patients were used as the training dataset. From another 20 prostate cancer patients, 210 CT images were used for independent validation of the segmentation method.

RESULTS

In the training dataset, 15 PCA modes were needed to represent 95% of shape variations of pelvic organs. When tested on the validation dataset, the authors' segmentation method had an average Dice similarity coefficient and mean absolute distance of 0.751 and 0.371 cm, 0.783 and 0.303 cm, 0.573 and 0.604 cm for prostate, bladder, and rectum, respectively. The automated segmentation process took on average 5 min on a personal computer equipped with Core 2 Duo CPU of 2.8 GHz and 8 GB RAM.

CONCLUSIONS

The authors have developed an efficient and reliable method for automatic segmentation of multiple organs in the male pelvis. This method should be useful for treatment planning and adaptive replanning for prostate cancer radiotherapy. With this method, the physicist can improve the work efficiency and stability.

Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a non-local external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation.

3D Transrectal Ultrasound (TRUS) Prostate Segmentation Based on Optimal Feature Learning Framework

We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework. Patient-specific anatomical features are extracted from aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to train the kernel support vector machine (KSVM). The well-trained SVM was used to localize the prostate of the new patient. Our segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentations (gold standard). The mean volume Dice overlap coefficient was 89.7%. In this study, we have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.

A Learning-Based CT Prostate Segmentation Method via Joint Transductive Feature Selection and Regression

In1 recent years, there has been a great interest in prostate segmentation, which is a important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician's simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms: tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice.

Analysis of topographical distribution of prostate cancer and related pathological findings in prostatectomy specimens using cMDX document architecture

INTRODUCTION

Understanding the topographical distribution of prostate cancer (PCa) foci is necessary to optimize the biopsy strategy. This study was done to develop a technical approach that facilitates the analysis of the topographical distribution of PCa foci and related pathological findings (i.e., Gleason score and foci dimensions) in prostatectomy specimens.

MATERIAL & METHODS

The topographical distribution of PCa foci and related pathologic evaluations were documented using the cMDX documentation system. The project was performed in three steps. First, we analyzed the document architecture of cMDX, including textual and graphical information. Second, we developed a data model supporting the topographic analysis of PCa foci and related pathologic parameters. Finally, we retrospectively evaluated the analysis model in 168 consecutive prostatectomy specimens of men diagnosed with PCa who underwent total prostate removal. The distribution of PCa foci were analyzed and visualized in a heat map. The color depth of the heat map was reduced to 6 colors representing the PCa foci frequencies, using an image posterization effect. We randomly defined 9 regions in which the frequency of PCa foci and related pathologic findings were estimated.

RESULTS

Evaluation of the spatial distribution of tumor foci according to Gleason score was enabled by using a filter function for the score, as defined by the user. PCa foci with Gleason score (Gls) 6 were identified in 67.3% of the patients, of which 55 (48.2%) also had PCa foci with Gls between 7 and 10. Of 1173 PCa foci, 557 had Gls 6, whereas 616 PCa foci had Gls>6. PCa foci with Gls 6 were mostly concentrated in the posterior part of the peripheral zone of the prostate, whereas PCa foci with Gls>6 extended toward the basal and anterior parts of the prostate. The mean size of PCa foci with Gls 6 was significantly lower than that of PCa with Gls>6 (P<0.0001).

CONCLUSION

The cMDX-based technical approach facilitates analysis of the topographical distribution of PCa foci and related pathologic findings in prostatectomy specimens.

Semi-automatic segmentation of prostate in CT images via coupled feature representation and spatial-constrained transductive lasso

Conventional learning-based methods for segmenting prostate in CT images ignore the relations among the low-level features by assuming all these features are independent. Also, their feature selection steps usually neglect the image appearance changes in different local regions of CT images. To this end, we present a novel semi-automatic learning-based prostate segmentation method in this article. For segmenting the prostate in a certain treatment image, the radiation oncologist will be first asked to take a few seconds to manually specify the first and last slices of the prostate. Then, prostate is segmented with the following two steps: (i) Estimation of 3D prostate-likelihood map to predict the likelihood of each voxel being prostate by employing the coupled feature representation, and the proposed Spatial-COnstrained Transductive LassO (SCOTO); (ii) Multi-atlases based label fusion to generate the final segmentation result by using the prostate shape information obtained from both planning and previous treatment images. The major contribution of the proposed method mainly includes: (i) incorporating radiation oncologist's manual specification to aid segmentation, (ii) adopting coupled features to relax previous assumption of feature independency for voxel representation, and (iii) developing SCOTO for joint feature selection across different local regions. The experimental result shows that the proposed method outperforms the state-of-the-art methods in a real-world prostate CT dataset, consisting of 24 patients with totally 330 images, all of which were manually delineated by the radiation oncologist for performance evaluation. Moreover, our method is also clinically feasible, since the segmentation performance can be improved by just requiring the radiation oncologist to spend only a few seconds for manual specification of ending slices in the current treatment CT image.

Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images

Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: (1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; (2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; (3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance.

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

PURPOSE

Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods.

METHODS

The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm.

RESULTS

The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency and the robustness. The automatic segmentation results with the original average Dice similarity coefficient of 0.78 were improved to 0.865-0.872 after conducting 55-59 interactions by using the proposed method, where each editing procedure took less than 3 s. In addition, the proposed method obtained the most consistent editing results with respect to different user interactions, compared to other methods.

CONCLUSIONS

The proposed method obtains robust editing results with few interactions for various wrong segmentation cases, by selecting the location-adaptive features and further imposing the manifold regularization. The authors expect the proposed method to largely reduce the laborious burdens of manual editing, as well as both the intra- and interobserver variability across clinicians.

Methodology to study the three-dimensional spatial distribution of prostate cancer and their dependence on clinical parameters

A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described. Three-dimensional (3-D) models of 216 glands are reconstructed from digital images of whole mount histopathological slices. The models are deformed into one prostate model selected as an atlas using a combination of rigid, affine, and B-spline deformable registration techniques. Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas. Finally, a difference between proportions is used to compare different spatial distributions. As a proof of concept, we compare spatial distributions from patients with PSA greater and less than [Formula: see text] and from patients older and younger than 60 years. Results suggest that prostate cancer has a significant difference in the right zone of the prostate between populations with PSA greater and less than [Formula: see text]. Age does not have any impact in the spatial distribution of the disease. The proposed methodology can help to comprehend prostate cancer by understanding its spatial distribution and how it changes according to clinical parameters. Finally, this methodology can be easily adapted to other organs and pathologies.

Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images

Accurate segmentation is usually crucial in transrectal ultrasound (TRUS) image based prostate diagnosis; however, it is always hampered by heavy speckles. Contrary to the traditional view that speckles are adverse to segmentation, we exploit intrinsic properties induced by speckles to facilitate the task, based on the observations that sizes and orientations of speckles provide salient cues to determine the prostate boundary. Since the speckle orientation changes in accordance with a statistical prior rule, rotation-invariant texture feature is extracted along the orientations revealed by the rule. To address the problem of feature changes due to different speckle sizes, TRUS images are split into several arc-like strips. In each strip, every individual feature vector is sparsely represented, and representation residuals are obtained. The residuals, along with the spatial coherence inherited from biological tissues, are combined to segment the prostate preliminarily via graph cuts. After that, the segmentation is fine-tuned by a novel level sets model, which integrates (1) the prostate shape prior, (2) dark-to-light intensity transition near the prostate boundary, and (3) the texture feature just obtained. The proposed method is validated on two 2-D image datasets obtained from two different sonographic imaging systems, with the mean absolute distance on the mid gland images only 1.06±0.53 mm and 1.25±0.77 mm, respectively. The method is also extended to segment apex and base images, producing competitive results over the state of the art.

Online updating of context-aware landmark detectors for prostate localization in daily treatment CT images

PURPOSE

In image guided radiation therapy, it is crucial to fast and accurately localize the prostate in the daily treatment images. To this end, the authors propose an online update scheme for landmark-guided prostate segmentation, which can fully exploit valuable patient-specific information contained in the previous treatment images and can achieve improved performance in landmark detection and prostate segmentation.

METHODS

To localize the prostate in the daily treatment images, the authors first automatically detect six anatomical landmarks on the prostate boundary by adopting a context-aware landmark detection method. Specifically, in this method, a two-layer regression forest is trained as a detector for each target landmark. Once all the newly detected landmarks from new treatment images are reviewed or adjusted (if necessary) by clinicians, they are further included into the training pool as new patient-specific information to update all the two-layer regression forests for the next treatment day. As more and more treatment images of the current patient are acquired, the two-layer regression forests can be continually updated by incorporating the patient-specific information into the training procedure. After all target landmarks are detected, a multiatlas random sample consensus (multiatlas RANSAC) method is used to segment the entire prostate by fusing multiple previously segmented prostates of the current patient after they are aligned to the current treatment image. Subsequently, the segmented prostate of the current treatment image is again reviewed (or even adjusted if needed) by clinicians before including it as a new shape example into the prostate shape dataset for helping localize the entire prostate in the next treatment image.

RESULTS

The experimental results on 330 images of 24 patients show the effectiveness of the authors' proposed online update scheme in improving the accuracies of both landmark detection and prostate segmentation. Besides, compared to the other state-of-the-art prostate segmentation methods, the authors' method achieves the best performance.

CONCLUSIONS

By appropriate use of valuable patient-specific information contained in the previous treatment images, the authors' proposed online update scheme can obtain satisfactory results for both landmark detection and prostate segmentation.

Prostatome: a combined anatomical and disease based MRI atlas of the prostate

PURPOSE

In this work, the authors introduce a novel framework, the anatomically constrained registration (AnCoR) scheme and apply it to create a fused anatomic-disease atlas of the prostate which the authors refer to as the prostatome. The prostatome combines a MRI based anatomic and a histology based disease atlas. Statistical imaging atlases allow for the integration of information across multiple scales and imaging modalities into a single canonical representation, in turn enabling a fused anatomical-disease representation which may facilitate the characterization of disease appearance relative to anatomic structures. While statistical atlases have been extensively developed and studied for the brain, approaches that have attempted to combine pathology and imaging data for study of prostate pathology are not extant. This works seeks to address this gap.

METHODS

The AnCoR framework optimizes a scoring function composed of two surface (prostate and central gland) misalignment measures and one intensity-based similarity term. This ensures the correct mapping of anatomic regions into the atlas, even when regional MRI intensities are inconsistent or highly variable between subjects. The framework allows for creation of an anatomic imaging and a disease atlas, while enabling their fusion into the anatomic imaging-disease atlas. The atlas presented here was constructed using 83 subjects with biopsy confirmed cancer who had pre-operative MRI (collected at two institutions) followed by radical prostatectomy. The imaging atlas results from mapping thein vivo MRI into the canonical space, while the anatomic regions serve as domain constraints. Elastic co-registration MRI and corresponding ex vivo histology provides "ground truth" mapping of cancer extent on in vivo imaging for 23 subjects.

RESULTS

AnCoR was evaluated relative to alternative construction strategies that use either MRI intensities or the prostate surface alone for registration. The AnCoR framework yielded a central gland Dice similarity coefficient (DSC) of 90%, and prostate DSC of 88%, while the misalignment of the urethra and verumontanum was found to be 3.45 mm, and 4.73 mm, respectively, which were measured to be significantly smaller compared to the alternative strategies. As might have been anticipated from our limited cohort of biopsy confirmed cancers, the disease atlas showed that most of the tumor extent was limited to the peripheral zone. Moreover, central gland tumors were typically larger in size, possibly because they are only discernible at a much later stage.

CONCLUSIONS

The authors presented the AnCoR framework to explicitly model anatomic constraints for the construction of a fused anatomic imaging-disease atlas. The framework was applied to constructing a preliminary version of an anatomic-disease atlas of the prostate, the prostatome. The prostatome could facilitate the quantitative characterization of gland morphology and imaging features of prostate cancer. These techniques, may be applied on a large sample size data set to create a fully developed prostatome that could serve as a spatial prior for targeted biopsies by urologists. Additionally, the AnCoR framework could allow for incorporation of complementary imaging and molecular data, thereby enabling their careful correlation for population based radio-omics studies.

Magnetic resonance imaging-targeted, 3D transrectal ultrasound-guided fusion biopsy for prostate cancer: Quantifying the impact of needle delivery error on diagnosis

PURPOSE

Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided "fusion" prostate biopsy intends to reduce the ∼23% false negative rate of clinical two-dimensional TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsies continue to yield false negatives. Therefore, the authors propose to investigate how biopsy system needle delivery error affects the probability of sampling each tumor, by accounting for uncertainties due to guidance system error, image registration error, and irregular tumor shapes.

METHODS

T2-weighted, dynamic contrast-enhanced T1-weighted, and diffusion-weighted prostate MRI and 3D TRUS images were obtained from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D tumor surfaces that were registered to the 3D TRUS images using an iterative closest point prostate surface-based method to yield 3D binary images of the suspicious regions in the TRUS context. The probabilityP of obtaining a sample of tumor tissue in one biopsy core was calculated by integrating a 3D Gaussian distribution over each suspicious region domain. Next, the authors performed an exhaustive search to determine the maximum root mean squared error (RMSE, in mm) of a biopsy system that gives P ≥ 95% for each tumor sample, and then repeated this procedure for equal-volume spheres corresponding to each tumor sample. Finally, the authors investigated the effect of probe-axis-direction error on measured tumor burden by studying the relationship between the error and estimated percentage of core involvement.

RESULTS

Given a 3.5 mm RMSE for contemporary fusion biopsy systems,P ≥ 95% for 21 out of 81 tumors. The authors determined that for a biopsy system with 3.5 mm RMSE, one cannot expect to sample tumors of approximately 1 cm(3) or smaller with 95% probability with only one biopsy core. The predicted maximum RMSE giving P ≥ 95% for each tumor was consistently greater when using spherical tumor shapes as opposed to no shape assumption. However, an assumption of spherical tumor shape for RMSE = 3.5 mm led to a mean overestimation of tumor sampling probabilities of 3%, implying that assuming spherical tumor shape may be reasonable for many prostate tumors. The authors also determined that a biopsy system would need to have a RMS needle delivery error of no more than 1.6 mm in order to sample 95% of tumors with one core. The authors' experiments also indicated that the effect of axial-direction error on the measured tumor burden was mitigated by the 18 mm core length at 3.5 mm RMSE.

CONCLUSIONS

For biopsy systems with RMSE ≥ 3.5 mm, more than one biopsy core must be taken from the majority of tumors to achieveP ≥ 95%. These observations support the authors' perspective that some tumors of clinically significant sizes may require more than one biopsy attempt in order to be sampled during the first biopsy session. This motivates the authors' ongoing development of an approach to optimize biopsy plans with the aim of achieving a desired probability of obtaining a sample from each tumor, while minimizing the number of biopsies. Optimized planning of within-tumor targets for MRI-3D TRUS fusion biopsy could support earlier diagnosis of prostate cancer while it remains localized to the gland and curable.

Improved image registration by sparse patch-based deformation estimation

Despite intensive efforts for decades, deformable image registration is still a challenging problem due to the potential large anatomical differences across individual images, which limits the registration performance. Fortunately, this issue could be alleviated if a good initial deformation can be provided for the two images under registration, which are often termed as the moving subject and the fixed template, respectively. In this work, we present a novel patch-based initial deformation prediction framework for improving the performance of existing registration algorithms. Our main idea is to estimate the initial deformation between subject and template in a patch-wise fashion by using the sparse representation technique. We argue that two image patches should follow the same deformation toward the template image if their patch-wise appearance patterns are similar. To this end, our framework consists of two stages, i.e., the training stage and the application stage. In the training stage, we register all training images to the pre-selected template, such that the deformation of each training image with respect to the template is known. In the application stage, we apply the following four steps to efficiently calculate the initial deformation field for the new test subject: (1) We pick a small number of key points in the distinctive regions of the test subject; (2) for each key point, we extract a local patch and form a coupled appearance-deformation dictionary from training images where each dictionary atom consists of the image intensity patch as well as their respective local deformations; (3) a small set of training image patches in the coupled dictionary are selected to represent the image patch of each subject key point by sparse representation. Then, we can predict the initial deformation for each subject key point by propagating the pre-estimated deformations on the selected training patches with the same sparse representation coefficients; and (4) we employ thin-plate splines (TPS) to interpolate a dense initial deformation field by considering all key points as the control points. Thus, the conventional image registration problem becomes much easier in the sense that we only need to compute the remaining small deformation for completing the registration of the subject to the template. Experimental results on both simulated and real data show that the registration performance can be significantly improved after integrating our patch-based deformation prediction framework into the existing registration algorithms.

Resolution enhancement of lung 4D-CT via group-sparsity

PURPOSE

4D-CT typically delivers more accurate information about anatomical structures in the lung, over 3D-CT, due to its ability to capture visual information of the lung motion across different respiratory phases. This helps to better determine the dose during radiation therapy for lung cancer. However, a critical concern with 4D-CT that substantially compromises this advantage is the low superior-inferior resolution due to less number of acquired slices, in order to control the CT radiation dose. To address this limitation, the authors propose an approach to reconstruct missing intermediate slices, so as to improve the superior-inferior resolution.

METHODS

In this method the authors exploit the observation that sampling information across respiratory phases in 4D-CT can be complimentary due to lung motion. The authors' approach uses this locally complimentary information across phases in a patch-based sparse-representation framework. Moreover, unlike some recent approaches that treat local patches independently, the authors' approach employs the group-sparsity framework that imposes neighborhood and similarity constraints between patches. This helps in mitigating the trade-off between noise robustness and structure preservation, which is an important consideration in resolution enhancement. The authors discuss the regularizing ability of group-sparsity, which helps in reducing the effect of noise and enables better structural localization and enhancement.

RESULTS

The authors perform extensive experiments on the publicly available DIR-Lab Lung 4D-CT dataset [R. Castillo, E. Castillo, R. Guerra, V. Johnson, T. McPhail, A. Garg, and T. Guerrero, "A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets," Phys. Med. Biol. 54, 1849-1870 (2009)]. First, the authors carry out empirical parametric analysis of some important parameters in their approach. The authors then demonstrate, qualitatively as well as quantitatively, the ability of their approach to achieve more accurate and better localized results over bicubic interpolation as well as a related state-of-the-art approach. The authors also show results on some datasets with tumor, to further emphasize the clinical importance of their method.

CONCLUSIONS

The authors have proposed to improve the superior-inferior resolution of 4D-CT by estimating intermediate slices. The authors' approach exploits neighboring constraints in the group-sparsity framework, toward the goal of achieving better localization and noise robustness. The authors' results are encouraging, and positively demonstrate the role of group-sparsity for 4D-CT resolution enhancement.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

PURPOSE

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation.

METHODS

To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace.

RESULTS

The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison.

CONCLUSIONS

A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

Incremental learning with selective memory (ILSM): towards fast prostate localization for image guided radiotherapy

Image-guided radiotherapy (IGRT) requires fast and accurate localization of the prostate in 3-D treatment-guided radiotherapy, which is challenging due to low tissue contrast and large anatomical variation across patients. On the other hand, the IGRT workflow involves collecting a series of computed tomography (CT) images from the same patient under treatment. These images contain valuable patient-specific information yet are often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to "personalize" the model to fit patient-specific appearance characteristics. The model is personalized with two steps: backward pruning that discards obsolete population-based knowledge and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of a specific patient much more accurately. This work has three contributions: 1) the proposed incremental learning framework can capture patient-specific characteristics more effectively, compared to traditional learning schemes, such as pure patient-specific learning, population-based learning, and mixture learning with patient-specific and population data; 2) this learning framework does not have any parametric model assumption, hence, allowing the adoption of any discriminative classifier; and 3) using ILSM, we can localize the prostate in treatment CTs accurately (DSC  ∼ 0.89 ) and fast (  ∼ 4 s), which satisfies the real-world clinical requirements of IGRT.

Statistical 3D Prostate Imaging Atlas Construction via Anatomically Constrained Registration

Statistical imaging atlases allow for integration of information from multiple patient studies collected across different image scales and modalities, such as multi-parametric (MP) MRI and histology, providing population statistics regarding a specific pathology within a single canonical representation. Such atlases are particularly valuable in the identification and validation of meaningful imaging signatures for disease characterization in vivo within a population. Despite the high incidence of prostate cancer, an imaging atlas focused on different anatomic structures of the prostate, i.e. an anatomic atlas, has yet to be constructed. In this work we introduce a novel framework for MRI atlas construction that uses an iterative, anatomically constrained registration (AnCoR) scheme to enable the proper alignment of the prostate (Pr) and central gland (CG) boundaries. Our current implementation uses endorectal, 1.5T or 3T, T2-weighted MRI from 51 patients with biopsy confirmed cancer; however, the prostate atlas is seamlessly extensible to include additional MRI parameters. In our cohort, radical prostatectomy is performed following MP-MR image acquisition; thus ground truth annotations for prostate cancer are available from the histological specimens. Once mapped onto MP-MRI through elastic registration of histological slices to corresponding T2-w MRI slices, the annotations are utilized by the AnCoR framework to characterize the 3D statistical distribution of cancer per anatomic structure. Such distributions are useful for guiding biopsies toward regions of higher cancer likelihood and understanding imaging profiles for disease extent in vivo. We evaluate our approach via the Dice similarity coefficient (DSC) for different anatomic structures (delineated by expert radiologists): Pr, CG and peripheral zone (PZ). The AnCoR-based atlas had a CG DSC of 90.36%, and Pr DSC of 89.37%. Moreover, we evaluated the deviation of anatomic landmarks, the urethra and veromontanum, and found 3.64 mm and respectively 4.31 mm. Alternative strategies that use only the T2-w MRI or the prostate surface to drive the registration were implemented as comparative approaches. The AnCoR framework outperformed the alternative strategies by providing the lowest landmark deviations.

Sparse patch-based label propagation for accurate prostate localization in CT images

In this paper, we propose a new prostate computed tomography (CT) segmentation method for image guided radiation therapy. The main contributions of our method lie in the following aspects. 1) Instead of using voxel intensity information alone, patch-based representation in the discriminative feature space with logistic sparse LASSO is used as anatomical signature to deal with low contrast problem in prostate CT images. 2) Based on the proposed patch-based signature, a new multi-atlases label fusion method formulated under sparse representation framework is designed to segment prostate in the new treatment images, with guidance from the previous segmented images of the same patient. This method estimates the prostate likelihood of each voxel in the new treatment image from its nearby candidate voxels in the previous segmented images, based on the nonlocal mean principle and sparsity constraint. 3) A hierarchical labeling strategy is further designed to perform label fusion, where voxels with high confidence are first labeled for providing useful context information in the same image for aiding the labeling of the remaining voxels. 4) An online update mechanism is finally adopted to progressively collect more patient-specific information from newly segmented treatment images of the same patient, for adaptive and more accurate segmentation. The proposed method has been extensively evaluated on a prostate CT image database consisting of 24 patients where each patient has more than 10 treatment images, and further compared with several state-of-the-art prostate CT segmentation algorithms using various evaluation metrics. Experimental results demonstrate that the proposed method consistently achieves higher segmentation accuracy than any other methods under comparison.

Prostate segmentation by sparse representation based classification

PURPOSE

The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems.

METHODS

To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by the elastic net in order to obtain a smooth and clear prostate boundary in the classification result. (3) Residue-based linear regression is incorporated to improve the classification performance and to extend SRC from hard classification to soft classification. (4) Iterative SRC is proposed by using context information to iteratively refine the classification results.

RESULTS

The proposed method has been comprehensively evaluated on a dataset consisting of 330 CT images from 24 patients. The effectiveness of the extended SRC has been validated by comparing it with the traditional SRC based on the proposed four extensions. The experimental results show that our extended SRC can obtain not only more accurate classification results but also smoother and clearer prostate boundary than the traditional SRC. Besides, the comparison with other five state-of-the-art prostate segmentation methods indicates that our method can achieve better performance than other methods under comparison.

CONCLUSIONS

The authors have proposed a novel prostate segmentation method based on the sparse representation based classification, which can achieve considerably accurate segmentation results in CT prostate segmentation.

Automated vs. manual pattern recognition of 3D (1)H MRSI data of patients with prostate cancer

RATIONALE AND OBJECTIVES

The aim of this study was to assess (1) automated analysis methods versus manual evaluation by human experts of three-dimensional proton magnetic resonance spectroscopic imaging (MRSI) data from patients with prostate cancer and (2) the contribution of spatial information to decision making.

MATERIALS AND METHODS

Three-dimensional proton MRSI was applied at 1.5 T. MRSI data from 10 patients with histologically proven prostate adenocarcinoma, scheduled either for prostatectomy or intensity-modulated radiation therapy, were evaluated. First, two readers manually labeled spectra using spatial information to identify the localization of spectra and neighborhood information, establishing the reference set of this study. Then, spectra were labeled again manually in a blinded and randomized manner and evaluated automatically using software that applied spectral line fitting as well as pattern recognition routines. Statistical analysis of the results of the different approaches was performed.

RESULTS

Altogether, 1018 spectra were evaluable by all methods. Numbers of evaluable spectra differed significantly depending on patient and evaluation method. Compared to automated analysis, the readers made rather binary decisions, using information from neighboring spectra in ambiguous cases, when evaluating MRSI data as a whole. Differences between anatomically blinded and unblinded evaluation were larger than differences between evaluations using blinded data and automated techniques.

CONCLUSIONS

An automated approach, which evaluates each spectrum individually, can be as good as an anatomy-blinded human reader. Spatial information is routinely used by human experts to support their final decisions. Automated procedures that consider anatomic information for spectral evaluation will enhance the diagnostic impact of MRSI of the human prostate.

INTEGRATING MRI AND MRSI INFORMATION INTO TRUS-GUIDED ROBOTIC PROSTATE BIOPSY

The development of prostate biopsy robotics can make biopsies both automatic and accurate. However, intervention from urologists is still needed to define the location of biopsy cores. With the aid of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging (MRSI) diagnosis information obtained pre-operationally, it is possible to guide the biopsy needle towards those sites where cancer is suspected, thereby achieving higher detection rates. In this paper, a deformable image registration method is presented for the purpose of merging MRI/MRSI and transrectal ultrasound (TRUS) images. Given the poor quality of ultrasound (US) images and the deformation occurring across modalites, a thin-plate spline transformation is used to match the prostate surfaces and thereafter their volumes. A deformable prostate phantom that simulates the condition in humans was also set up for validation purposes. Fifteen fiducial markers were implanted inside the phantom prostate to act as the reference of "ground truth." The phantom study shows that our method can achieve an accuracy around 1.28 ± 0.50 mm, with voxel dimensions of 0.5 × 0.5 × 0.5 mm3. This result is promising since none of the knowledge about the interior prostate is utilized in the algorithm. Experimental results on patient data are also presented.

Automated preoperative planning of femoral stem in total hip arthroplasty from 3D CT data: atlas-based approach and comparative study

Atlas-based methods for automated preoperative planning of the femoral stem implant in total hip arthroplasty are described. Statistical atlases are constructed from a number of past preoperative plans prepared by experienced surgeons in order to represent the surgeon's expertise of the planning. Two types of atlases are considered. One is a statistical distance map atlas, which represents surgeon's preference of the contact pattern between the femoral canal (host bone) and stem (implant) surfaces. The other is an optimal reference plan, which is selected as the best representative plan expected to minimize the deviation from the surgeon's preferred contact pattern. These atlases are fitted to the patient data to automatically generate the preoperative plan of the femoral stem. In this paper, we formulate a general framework of atlas-based implant planning, and then describe the methods for construction and utilization of the two proposed atlases. In the experiments, we used 40 cases to evaluate the proposed methods and compare them with previous methods by defining the errors as differences between automated and surgeon's plans. By using the proposed methods, the positional and orientation errors were significantly reduced compared with the previous methods and the size error was superior to inter-surgeon variability in size selection using 2D templates on an X-ray image reported in previous work.

ProstAtlas: a digital morphologic atlas of the prostate

Computer-aided medical interventions and medical robotics for prostate cancer have known an increasing interest and research activity. However before the routine deployment of these procedures in clinical practice becomes a reality, in vivo and in silico validations must be undertaken. In this study, we developed a digital morphologic atlas of the prostate. We were interested by the gland, the peripheral zone and the central gland. Starting from an image base collected from 30 selected patients, a mean shape and most important deformations for each structure were deduced using principal component analysis. The usefulness of this atlas was highlighted in two applications: image simulation and physical phantom design.

Adaptively learning local shape statistics for prostate segmentation in ultrasound

Automatic segmentation of the prostate from 2-D transrectal ultrasound (TRUS) is a highly desired tool in many clinical applications. However, it is a very challenging task, especially for segmenting the base and apex of the prostate due to the large shape variations in those areas compared to the midgland, which leads many existing segmentation methods to fail. To address the problem, this paper presents a novel TRUS video segmentation algorithm using both global population-based and patient-specific local shape statistics as shape constraint. By adaptively learning shape statistics in a local neighborhood during the segmentation process, the algorithm can effectively capture the patient-specific shape statistics and quickly adapt to the local shape changes in the base and apex areas. The learned shape statistics is then used as the shape constraint in a deformable model for TRUS video segmentation. The proposed method can robustly segment the entire gland of the prostate with significantly improved performance in the base and apex regions, compared to other previously reported methods. Our method was evaluated using 19 video sequences obtained from different patients and the average mean absolute distance error was 1.65 ± 0.47 mm.

Discrete deformable model guided by partial active shape model for TRUS image segmentation

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is highly desired in many clinical applications. However, robust and automated prostate segmentation is challenging due to the low SNR in TRUS and the missing boundaries in shadow areas caused by calcifications or hyperdense prostate tissues. This paper presents a novel method of utilizing a priori shapes estimated from partial contours for segmenting the prostate. The proposed method is able to automatically extract prostate boundary from 2-D TRUS images without user interaction for shape correction in shadow areas. During the segmentation process, missing boundaries in shadow areas are estimated by using a partial active shape model, which takes partial contours as input but returns a complete shape estimation. With this shape guidance, an optimal search is performed by a discrete deformable model to minimize an energy functional for image segmentation, which is achieved efficiently by using dynamic programming. The segmentation of an image is executed in a multiresolution fashion from coarse to fine for robustness and computational efficiency. Promising segmentation results were demonstrated on 301 TRUS images grabbed from 19 patients with the average mean absolute distance error of 2.01 mm +/- 1.02 mm.

Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score

Prostate biopsy is the current gold-standard procedure for prostate cancer diagnosis. Existing prostate biopsy procedures have been mostly focusing on detecting cancer presence. However, they often ignore the potential use of biopsy to estimate cancer volume (CV) and Gleason Score (GS, a cancer grade descriptor), the two surrogate markers for cancer aggressiveness and the two crucial factors for treatment planning. To fill up this vacancy, this paper assumes and demonstrates that, by optimally sampling the spatial patterns of cancer, biopsy procedures can be specifically designed for estimating CV and GS. Our approach combines image analysis and machine learning tools in an atlas-based population study that consists of three steps. First, the spatial distributions of cancer in a patient population are learned, by constructing statistical atlases from histological images of prostate specimens with known cancer ground truths. Then, the optimal biopsy locations are determined in a feature selection formulation, so that biopsy outcomes (either cancer presence or absence) at those locations could be used to differentiate, at the best rate, between the existing specimens having different (high vs. low) CV/GS values. Finally, the optimized biopsy locations are utilized to estimate whether a new-coming prostate cancer patient has high or low CV/GS values, based on a binary classification formulation. The estimation accuracy and the generalization ability are evaluated by the classification rates and the associated receiver-operating-characteristic (ROC) curves in cross validations. The optimized biopsy procedures are also designed to be robust to the almost inevitable needle displacement errors in clinical practice, and are found to be robust to variations in the optimization parameters as well as the training populations.

Adaptation of a 3D prostate cancer atlas for transrectal ultrasound guided target-specific biopsy

Due to lack of imaging modalities to identify prostate cancer in vivo, current TRUS guided prostate biopsies are taken randomly. Consequently, many important cancers are missed during initial biopsies. The purpose of this study was to determine the potential clinical utility of a high-speed registration algorithm for a 3D prostate cancer atlas. This 3D prostate cancer atlas provides voxel-level likelihood of cancer and optimized biopsy locations on a template space (Zhan et al 2007). The atlas was constructed from 158 expert annotated, 3D reconstructed radical prostatectomy specimens outlined for cancers (Shen et al 2004). For successful clinical implementation, the prostate atlas needs to be registered to each patient's TRUS image with high registration accuracy in a time-efficient manner. This is implemented in a two-step procedure, the segmentation of the prostate gland from a patient's TRUS image followed by the registration of the prostate atlas. We have developed a fast registration algorithm suitable for clinical applications of this prostate cancer atlas. The registration algorithm was implemented on a graphical processing unit (GPU) to meet the critical processing speed requirements for atlas guided biopsy. A color overlay of the atlas superposed on the TRUS image was presented to help pick statistically likely regions known to harbor cancer. We validated our fast registration algorithm using computer simulations of two optimized 7- and 12-core biopsy protocols to maximize the overall detection rate. Using a GPU, patient's TRUS image segmentation and atlas registration took less than 12 s. The prostate cancer atlas guided 7- and 12-core biopsy protocols had cancer detection rates of 84.81% and 89.87% respectively when validated on the same set of data. Whereas the sextant biopsy approach without the utility of 3D cancer atlas detected only 70.5% of the cancers using the same histology data. We estimate 10-20% increase in prostate cancer detection rates when TRUS guided biopsies are assisted by the 3D prostate cancer atlas compared to the current standard of care. The fast registration algorithm we have developed can easily be adapted for clinical applications for the improved diagnosis of prostate cancer.

Three-dimensional sonography with needle tracking: role in diagnosis and treatment of prostate cancer

OBJECTIVE

Image-guided prostate biopsy has become routine in medical diagnosis. Although it improves biopsy outcome, it mostly operates in 2 dimensions, therefore lacking presentation of information in the complete 3-dimensional (3D) space. Because prostatic carcinomas are nonuniformly distributed within the prostate gland, it is crucial to accurately guide the needles toward clinically important locations within the 3D volume for both diagnosis and treatment.

METHODS

We reviewed the uses of 3D image-guided needle procedures in prostate cancer diagnosis and cancer therapy as well as their advantages, work flow, and future directions.

RESULTS

Guided procedures for the prostate rely on accurate 3D target identification and needle navigation. This 3D approach has potential for better disease diagnosis and therapy. Additionally, when fusing together different imaging modalities and cancer probability maps obtained from a population of interest, physicians can potentially place biopsy needles and other interventional devices more accurately and efficiently by better targeting regions that are likely to host cancerous tissue.

CONCLUSIONS

With the information from anatomic, metabolic, functional, biochemical, and biomechanical statuses of different regions of the entire gland, prostate cancers will be better diagnosed and treated with improved work flow.

Transperineal prostate biopsy under magnetic resonance image guidance: a needle placement accuracy study

PURPOSE

To quantify needle placement accuracy of magnetic resonance image (MRI)-guided core needle biopsy of the prostate.

MATERIALS AND METHODS

A total of 10 biopsies were performed with 18-gauge (G) core biopsy needle via a percutaneous transperineal approach. Needle placement error was assessed by comparing the coordinates of preplanned targets with the needle tip measured from the intraprocedural coherent gradient echo images. The source of these errors was subsequently investigated by measuring displacement caused by needle deflection and needle susceptibility artifact shift in controlled phantom studies. Needle placement error due to misalignment of the needle template guide was also evaluated.

RESULTS

The mean and standard deviation (SD) of errors in targeted biopsies was 6.5 +/- 3.5 mm. Phantom experiments showed significant placement error due to needle deflection with a needle with an asymmetrically beveled tip (3.2-8.7 mm depending on tissue type) but significantly smaller error with a symmetrical bevel (0.6-1.1 mm). Needle susceptibility artifacts observed a shift of 1.6 +/- 0.4 mm from the true needle axis. Misalignment of the needle template guide contributed an error of 1.5 +/- 0.3 mm.

CONCLUSION

Needle placement error was clinically significant in MRI-guided biopsy for diagnosis of prostate cancer. Needle placement error due to needle deflection was the most significant cause of error, especially for needles with an asymmetrical bevel.

Computer-aided diagnosis of prostate cancer with emphasis on ultrasound-based approaches: a review

This paper reviews the state of the art in computer-aided diagnosis of prostate cancer and focuses, in particular, on ultrasound-based techniques for detection of cancer in prostate tissue. The current standard procedure for diagnosis of prostate cancer, i.e., ultrasound-guided biopsy followed by histopathological analysis of tissue samples, is invasive and produces a high rate of false negatives resulting in the need for repeated trials. It is against these backdrops that the search for new methods to diagnose prostate cancer continues. Image-based approaches (such as MRI, ultrasound and elastography) represent a major research trend for diagnosis of prostate cancer. Due to the integration of ultrasound imaging in the current clinical procedure for detection of prostate cancer, we specifically provide a more detailed review of methodologies that use ultrasound RF-spectrum parameters, B-scan texture features and Doppler measures for prostate tissue characterization. We present current and future directions of research aimed at computer-aided detection of prostate cancer and conclude that ultrasound is likely to play an important role in the field.

Targeted prostate biopsy using statistical image analysis

In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.

Challenges in image-guided therapy system design

System development for image-guided therapy (IGT), or image-guided interventions (IGI), continues to be an area of active interest across academic and industry groups. This is an emerging field that is growing rapidly: major academic institutions and medical device manufacturers have produced IGT technologies that are in routine clinical use, dozens of high-impact publications are published in well regarded journals each year, and several small companies have successfully commercialized sophisticated IGT systems. In meetings between IGT investigators over the last two years, a consensus has emerged that several key areas must be addressed collaboratively by the community to reach the next level of impact and efficiency in IGT research and development to improve patient care. These meetings culminated in a two-day workshop that brought together several academic and industrial leaders in the field today. The goals of the workshop were to identify gaps in the engineering infrastructure available to IGT researchers, develop the role of research funding agencies and the recently established US-based National Center for Image Guided Therapy (NCIGT), and ultimately to facilitate the transfer of technology among research centers that are sponsored by the National Institutes of Health (NIH). Workshop discussions spanned many of the current challenges in the development and deployment of new IGT systems. Key challenges were identified in a number of areas, including: validation standards; workflows, use-cases, and application requirements; component reusability; and device interface standards. This report elaborates on these key points and proposes research challenges that are to be addressed by a joint effort between academic, industry, and NIH participants.

Registering histological and MR images of prostate for image-based cancer detection

This paper presents a deformable registration method to co-register histological images with MR images of the same prostate. By considering various distortion and cutting artifacts in histological images and also fundamentally different nature of histological and MR images, our registration method is thus guided by two types of landmark points that can be reliably detected in both histological and MR images, i.e., prostate boundary points, and internal salient points that can be identified by a scale-space analysis method. The similarity between these automatically detected landmarks in histological and MR images are defined by geometric features and normalized mutual information, respectively. By optimizing a function, which integrates the similarities between landmarks with spatial constraints, the correspondences between the landmarks as well as the deformable transformation between histological and MR images can be simultaneously obtained. The performance of our proposed registration algorithm has been evaluated by various designed experiments. This work is part of a larger effort to develop statistical atlases of prostate cancer using both imaging and histological information, and to use these atlases for optimal biopsy and therapy planning.

Ultrasound image segmentation: a survey

This paper reviews ultrasound segmentation paper methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.

A protocol for evaluation of similarity measures for rigid registration

The accuracy and robustness of a registration method depend on a number of factors, such as imaging modality, image content and image degrading effects, the class of spatial transformation used for registration, similarity measure, optimization, and numerous implementation details. The complex interdependence of these factors makes the assessment of the influence of a particular factor on registration difficult, although it is often desirable to have some estimate of such influences prior to registration. The similarity measure used to create the cost function is one of the factors that most influences the quality of registration. Traditionally, limited information on the behavior of a similarity measure is obtained either by studying the quality of the final registration or by drawing plots of similarity measure values obtained by translating or rotating one image relative to the "gold standard." In this paper, we present a protocol for a more thorough, optimization-independent, and systematic statistical evaluation of similarity measures. This protocol estimates a similarity measure's capture range, the number, location and extent of local optima, and the accuracy and distinctiveness of the global optimum. To show that the proposed evaluation protocol is viable, we have conducted several experiments with nine similarity measures and real computed tomography and magnetic resonance (MR) images of a spine phantom, MR brain images, and MR and positron emission tomography brain images, for which "gold standard" registrations were available. We have also studied the impact of histogram bin size on the behavior of nine similarity measures. The proposed evaluation protocol is useful for selecting the best similarity measure and corresponding optimization method for a particular application, as well as for studying the influence of sampling, interpolation, histogram bin size, partial image overlap, and image degradation, such as noise, intensity inhomogeneity, and geometrical distortions on the behavior of a similarity measure.

Robotically assisted prostate brachytherapy with transrectal ultrasound guidance--Phantom experiments

PURPOSE

To report the preliminary experimental results obtained with a robot-assisted transrectal ultrasound (TRUS)-guided prostate brachytherapy system.

METHODS AND MATERIALS

The system consists of a TRUS unit, a spatially coregistered needle insertion robot, and an FDA-approved treatment planning and image-registered implant system. The robot receives each entry/target coordinate pair of the implant plan, inserts a preloaded needle, and then the seeds are deposited. The needles/sources are tracked in TRUS, thus allowing the plan to be updated as the procedure progresses.

RESULTS

The first insertion attempt was recorded for each needle, without adjustment. All clinically relevant locations were reached in a prostate phantom. Nonparallel and parallel needle trajectories were demonstrated. Based on TRUS, the average transverse placement error was 2 mm (worst case 2.5 mm, 80% less than 2 mm), and the average sagittal error was 2.5 mm (worst case 5.0 mm, 70% less than 2.5 mm).

CONCLUSIONS

The concept and technical viability of robot-assisted brachytherapy were demonstrated in phantoms. The kinematically decoupled robotic assistant device is inherently safe. Overall performance was promising, but further optimization is necessary to prove the possibility of improved dosimetry.

Computer technology in detection and staging of prostate carcinoma: A review

After two decades of increasing interest and research activity, computer-assisted diagnostic approaches are reaching the stage where more routine deployment in clinical practice is becoming a possibility [Kruppinski, E.A., 2004. Computer-aided detection in clinical environment: Benefits and challenges for radiologists. Radiology 231, 7-9]. This is particularly the case in the analysis of mammographic images [Helvie, M.A., Hadjiiski, L., Makariou, E., Chan, H.P., Petrick, N., Sahiner, B., Lo, S.C., Freedman, M., Adler, D., Bailey, J., Blane, C., Hoff, D., Hunt, K., Joynt, L., Klein, K., Paramagul, C., Patterson, S.K., Roubidoux, M.A., 2004. Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial. Radiology 231, 208-214] and in the detection of pulmonary nodules [Reeves, A.P., Kostis, W.J., 2000. Computer-aided diagnosis for lung cancer. Radiol. Clin. North Am. 38, 497-509]. However, similar approaches can be applied more widely with the promise of increasing clinical utility in other areas. We review how computer-aided approaches may be applied in the diagnosis and staging of prostatic cancer. The current status of computer technology is reviewed, covering artificial neural networks for detection and staging, computerised biopsy simulation and computer-assisted analysis of ultrasound and magnetic resonance images.