Atlas-based prostate segmentation using an hybrid registration

In silico trial of simulation-free hippocampal-avoidance whole brain adaptive radiotherapy

Background and Purpose

Hippocampal-avoidance whole brain radiotherapy (HA-WBRT) can be a time-consuming process compared to conventional whole brain techniques, thus potentially limiting widespread utilization. Therefore, we evaluated the in silico clinical feasibility, via dose-volume metrics and timing, by leveraging a computed tomography (CT)-based commercial adaptive radiotherapy (ART) platform and workflow in order to create and deliver patient-specific, simulation-free HA-WBRT.

Materials and methods

Ten patients previously treated for central nervous system cancers with cone-beam computed tomography (CBCT) imaging were included in this study. The CBCT was the adaptive image-of-the-day to simulate first fraction on-board imaging. Initial contours defined on the MRI were rigidly matched to the CBCT. Online ART was used to create treatment plans at first fraction. Dose-volume metrics of these simulation-free plans were compared to standard-workflow HA-WBRT plans on each patient CT simulation dataset. Timing data for the adaptive planning sessions were recorded.

Results

For all ten patients, simulation-free HA-WBRT plans were successfully created utilizing the online ART workflow and met all constraints. The median hippocampi D100% was 7.8 Gy (6.6-8.8 Gy) in the adaptive plan vs 8.1 Gy (7.7-8.4 Gy) in the standard workflow plan. All plans required adaptation at first fraction due to both a failing hippocampal constraint (6/10 adaptive fractions) and sub-optimal target coverage (6/10 adaptive fractions). Median time for the adaptive session was 45.2 min (34.0-53.8 min).

Conclusions

Simulation-free HA-WBRT, with commercially available systems, was clinically feasible via plan-quality metrics and timing, in silico.

CT synthesis from MR in the pelvic area using Residual Transformer Conditional GAN

Magnetic resonance (MR) image-guided radiation therapy is a hot topic in current radiation therapy research, which relies on MR to generate synthetic computed tomography (SCT) images for radiation therapy. Convolution-based generative adversarial networks (GAN) have achieved promising results in synthesizing CT from MR since the introduction of deep learning techniques. However, due to the local limitations of pure convolutional neural networks (CNN) structure and the local mismatch between paired MR and CT images, particularly in pelvic soft tissue, the performance of GAN in synthesizing CT from MR requires further improvement. In this paper, we propose a new GAN called Residual Transformer Conditional GAN (RTCGAN), which exploits the advantages of CNN in local texture details and Transformer in global correlation to extract multi-level features from MR and CT images. Furthermore, the feature reconstruction loss is used to further constrain the image potential features, reducing over-smoothing and local distortion of the SCT. The experiments show that RTCGAN is visually closer to the reference CT (RCT) image and achieves desirable results on local mismatch tissues. In the quantitative evaluation, the MAE, SSIM, and PSNR of RTCGAN are 45.05 HU, 0.9105, and 28.31 dB, respectively. All of them outperform other comparison methods, such as deep convolutional neural networks (DCNN), Pix2Pix, Attention-UNet, WPD-DAGAN, and HDL.

A novel registration-based algorithm for prostate segmentation via the combination of SSM and CNN

PURPOSE

Precise determination of target is an essential procedure in prostate interventions, such as prostate biopsy, lesion detection, and targeted therapy. However, the prostate delineation may be tough in some cases due to tissue ambiguity or lack of partial anatomical boundary. In this study, we proposed a novel supervised registration-based algorithm for precise prostate segmentation, which combine the convolutional neural network (CNN) with a statistical shape model (SSM).

METHODS

The proposed network mainly consists of two branches. One called SSM-Net branch was exploited to predict the shape transform matrix, shape control parameters, and shape fine-tuning vector, for the generation of the prostate boundary. Furtherly, according to the inferred boundary, a normalized distance map was calculated as the output of SSM-Net. Another branch named ResU-Net was employed to predict a probability label map from the input images at the same time. Integrating the output of these two branches, the optimal weighted sum of the distance map and the probability map was regarded as the prostate segmentation.

RESULTS

Two public datasets PROMISE12 and NCI-ISBI 2013 were utilized to evaluate the performance of the proposed algorithm. The results demonstrate that the segmentation algorithm achieved the best performance with an SSM of 9500 nodes, which obtained a dice of 0.907 and an average surface distance of 1.85 mm. Compared with other methods, our algorithm delineates the prostate region more accurately and efficiently. In addition, we verified the impact of model elasticity augmentation and the fine-tuning item on the network segmentation capability. As a result, both factors have improved the delineation accuracy, with dice increased by 10% and 7% respectively.

CONCLUSIONS

Our segmentation method has the potential to be an effective and robust approach for prostate segmentation. This article is protected by copyright. All rights reserved.

Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net

PURPOSE

Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle placement, brachytherapy treatment planning, and motion management. Manual segmentation during these interventions is time-consuming and subject to inter- and intraobserver variation. To address these drawbacks, we aimed to develop a deep learning-based method which integrates deep supervision into a three-dimensional (3D) patch-based V-Net for prostate segmentation.

METHODS AND MATERIALS

We developed a multidirectional deep-learning-based method to automatically segment the prostate for ultrasound-guided radiation therapy. A 3D supervision mechanism is integrated into the V-Net stages to deal with the optimization difficulties when training a deep network with limited training data. We combine a binary cross-entropy (BCE) loss and a batch-based Dice loss into the stage-wise hybrid loss function for a deep supervision training. During the segmentation stage, the patches are extracted from the newly acquired ultrasound image as the input of the well-trained network and the well-trained network adaptively labels the prostate tissue. The final segmented prostate volume is reconstructed using patch fusion and further refined through a contour refinement processing.

RESULTS

Forty-four patients' TRUS images were used to test our segmentation method. Our segmentation results were compared with the manually segmented contours (ground truth). The mean prostate volume Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and residual mean surface distance (RMSD) were 0.92 ± 0.03, 3.94 ± 1.55, 0.60 ± 0.23, and 0.90 ± 0.38 mm, respectively.

CONCLUSION

We developed a novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for diagnostic and therapeutic applications in prostate cancer.

GAS: A genetic atlas selection strategy in multi-atlas segmentation framework

Multi-Atlas based Segmentation (MAS) algorithms have been successfully applied to many medical image segmentation tasks, but their success relies on a large number of atlases and good image registration performance. Choosing well-registered atlases for label fusion is vital for an accurate segmentation. This choice becomes even more crucial when the segmentation involves organs characterized by a high anatomical and pathological variability. In this paper, we propose a new genetic atlas selection strategy (GAS) that automatically chooses the best subset of atlases to be used for segmenting the target image, on the basis of both image similarity and segmentation overlap. More precisely, the key idea of GAS is that if two images are similar, the performances of an atlas for segmenting each image are similar. Since the ground truth of each atlas is known, GAS first selects a predefined number of similar images to the target, then, for each one of them, finds a near-optimal subset of atlases by means of a genetic algorithm. All these near-optimal subsets are then combined and used to segment the target image. GAS was tested on single-label and multi-label segmentation problems. In the first case, we considered the segmentation of both the whole prostate and of the left ventricle of the heart from magnetic resonance images. Regarding multi-label problems, the zonal segmentation of the prostate into peripheral and transition zone was considered. The results showed that the performance of MAS algorithms statistically improved when GAS is used.

Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory

PURPOSE

In this article, we propose a novel, semi-automatic segmentation method to process 3D MR images of the prostate using the Bhattacharyya coefficient and active band theory with the goal of providing technical support for computer-aided diagnosis and surgery of the prostate.

METHODS

Our method consecutively segments a stack of rotationally resectioned 2D slices of a prostate MR image by assessing the similarity of the shape and intensity distribution in neighboring slices. 2D segmentation is first performed on an initial slice by manually selecting several points on the prostate boundary, after which the segmentation results are propagated consecutively to neighboring slices. A framework of iterative graph cuts is used to optimize the energy function, which contains a global term for the Bhattacharyya coefficient with the help of an auxiliary function. Our method does not require previously segmented data for training or for building statistical models, and manual intervention can be applied flexibly and intuitively, indicating the potential utility of this method in the clinic.

RESULTS

We tested our method on 3D T2-weighted MR images from the ISBI dataset and PROMISE12 dataset of 129 patients, and the Dice similarity coefficients were 90.34 ± 2.21% and 89.32 ± 3.08%, respectively. The comparison was performed with several state-of-the-art methods, and the results demonstrate that the proposed method is robust and accurate, achieving similar or higher accuracy than other methods without requiring training.

CONCLUSION

The proposed algorithm for segmenting 3D MR images of the prostate is accurate, robust, and readily applicable to a clinical environment for computer-aided surgery or diagnosis.

Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging

Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ∼60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm³ in the mid-gland. Concordant with other studies, accuracy was highest in the mid-gland and lower in the apex and base. Loss of accuracy with respect to the semi-automatic algorithm was less than the measured inter-observer variability in manual segmentation for the same task.

A non-rigid registration method for the analysis of local deformations in the wood cell wall

This paper concerns the problem of wood cellular structure image registration. Given the large variability of wood geometry and the important changes in the cellular organization due to moisture sorption, an affine-based image registration technique is not exhaustive to describe the overall hygro-mechanical behaviour of wood at micrometre scales. Additionally, free tools currently available for non-rigid image registration are not suitable for quantifying the structural deformations of complex hierarchical materials such as wood, leading to errors due to misalignment. In this paper, we adapt an existing non-rigid registration model based on B-spline functions to our case study. The so-modified algorithm combines the concept of feature recognition within specific regions locally distributed in the material with an optimization problem. Results show that the method is able to quantify local deformations induced by moisture changes in tomographic images of wood cell wall with high accuracy. The local deformations provide new important insights in characterizing the swelling behaviour of wood at the cell wall level.

Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy

BACKGROUND AND PURPOSE

Segmentation of intra-prostatic urethra for dose assessment from planning CT may help explaining urinary toxicity in prostate cancer radiotherapy. This work sought to: i) propose an automatic method for urethra segmentation in CT, ii) compare it with previously proposed surrogate models and iii) quantify the dose received by the urethra in patients treated with IMRT.

MATERIALS AND METHODS

A weighted multi-atlas-based urethra segmentation method was devised from a training data set of 55 CT scans of patients receiving brachytherapy with visible urinary catheters. Leave-one-out cross validation was performed to quantify the error between the urethra segmentation and the catheter ground truth with two scores: the centerlines distance (CLD) and the percentage of centerline within a certain distance from the catheter (PWR). The segmentation method was then applied to a second test data set of 95 prostate cancer patients having received 78Gy IMRT to quantify dose to the urethra.

RESULTS

Mean CLD was 3.25±1.2mm for the whole urethra and 3.7±1.7mm, 2.52±1.5mm, and 3.01±1.7mm for the top, middle, and bottom thirds, respectively. In average, 53% of the segmented centerlines were within a radius<3.5mm from the centerline ground truth and 83% in a radius<5mm. The proposed method outperformed existing surrogate models. In IMRT, urethra DVH was significantly higher than prostate DVH from V74Gy to V79Gy.

CONCLUSION

A multi-atlas-based segmentation method was proposed enabling assessment of the dose within the prostatic urethra.

A multiresolution prostate representation for automatic segmentation in magnetic resonance images

PURPOSE

Accurate prostate delineation is necessary in radiotherapy processes for concentrating the dose onto the prostate and reducing side effects in neighboring organs. Currently, manual delineation is performed over magnetic resonance imaging (MRI) taking advantage of its high soft tissue contrast property. Nevertheless, as human intervention is a consuming task with high intra- and interobserver variability rates, (semi)-automatic organ delineation tools have emerged to cope with these challenges, reducing the time spent for these tasks. This work presents a multiresolution representation that defines a novel metric and allows to segment a new prostate by combining a set of most similar prostates in a dataset.

METHODS

The proposed method starts by selecting the set of most similar prostates with respect to a new one using the proposed multiresolution representation. This representation characterizes the prostate through a set of salient points, extracted from a region of interest (ROI) that encloses the organ and refined using structural information, allowing to capture main relevant features of the organ boundary. Afterward, the new prostate is automatically segmented by combining the nonrigidly registered expert delineations associated to the previous selected similar prostates using a weighted patch-based strategy. Finally, the prostate contour is smoothed based on morphological operations.

RESULTS

The proposed approach was evaluated with respect to the expert manual segmentation under a leave-one-out scheme using two public datasets, obtaining averaged Dice coefficients of 82% ± 0.07 and 83% ± 0.06, and demonstrating a competitive performance with respect to atlas-based state-of-the-art methods.

CONCLUSIONS

The proposed multiresolution representation provides a feature space that follows a local salient point criteria and a global rule of the spatial configuration among these points to find out the most similar prostates. This strategy suggests an easy adaptation in the clinical routine, as supporting tool for annotation.

[Segmentation of the prostate on magnetic resonance images using an ellipsoidal shape prior constraint algorithm]

We propose a novel strategy for multi-atlas-based image segmentation of the prostate on magnetic resonance (MR) images using an ellipsoidal shape prior constraint algorithm. An ellipsoidal shape prior constraint was incorporated into the process of multi-atlas based segmentation to restrict the regions of interest on the prostate images and avoid the interference by the surrounding tissues and organs in atlas selection. In the subsequent process of atlas fusion, the ellipsoidal shape prior constraint calibrated and compensated for the shape prior obtained by the registration technique to avoid incorrect segmentation caused by registration errors. Evaluation of this proposed method on prostate images from 50 subjects showed that this algorithm was effective and yielded a mean Dice similarity coefficients of 0.8812, suggesting its high accuracy and robustness to segment the prostate on MR images.

Postediting prostate magnetic resonance imaging segmentation consistency and operator time using manual and computer-assisted segmentation: multiobserver study

Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre- and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328, and 393 s for manual, semiautomatic, and automatic segmentation respectively, while an average fully manual segmentation time of 564 s was recorded. The reduced measured postediting interoperator variability of semiautomatic and automatic segmentations compared to the manual approach indicates the potential of computer-assisted segmentation for generating a clinically acceptable segmentation faster with higher consistency. The lack of strong correlation between editing time and the values of typically used error metrics ([Formula: see text]) implies that the necessary postsegmentation editing time needs to be measured directly in order to evaluate an algorithm's suitability for clinical translation.

A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features

Blurred boundaries and heterogeneous intensities make accurate prostate MR image segmentation problematic. To improve prostate MR image segmentation we suggest an approach that includes: (a) an image patch division method to partition the prostate into homogeneous segments for feature extraction; (b) an image feature formulation and classification method, using the relevance vector machine, to provide probabilistic prior knowledge for graph energy construction; (c) a graph energy formulation scheme with Bayesian priors and Dirichlet graph energy and (d) a non-iterative graph energy minimization scheme, based on matrix differentiation, to perform the probabilistic pixel membership optimization. The segmentation output was obtained by assigning pixels with foreground and background labels based on derived membership probabilities. We evaluated our approach on the PROMISE-12 dataset with 50 prostate MR image volumes. Our approach achieved a mean dice similarity coefficient (DSC) of 0.90  ±  0.02, which surpassed the five best prior-based methods in the PROMISE-12 segmentation challenge.

The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

PURPOSE

An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer.

METHODS

A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T2-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas and intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework.

RESULTS

A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved.

CONCLUSIONS

This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.

Spatially varying accuracy and reproducibility of prostate segmentation in magnetic resonance images using manual and semiautomated methods

PURPOSE

Three-dimensional (3D) prostate image segmentation is useful for cancer diagnosis and therapy guidance, but can be time-consuming to perform manually and involves varying levels of difficulty and interoperator variability within the prostatic base, midgland (MG), and apex. In this study, the authors measured accuracy and interobserver variability in the segmentation of the prostate on T2-weighted endorectal magnetic resonance (MR) imaging within the whole gland (WG), and separately within the apex, midgland, and base regions.

METHODS

The authors collected MR images from 42 prostate cancer patients. Prostate border delineation was performed manually by one observer on all images and by two other observers on a subset of ten images. The authors used complementary boundary-, region-, and volume-based metrics [mean absolute distance (MAD), Dice similarity coefficient (DSC), recall rate, precision rate, and volume difference (ΔV)] to elucidate the different types of segmentation errors that they observed. Evaluation for expert manual and semiautomatic segmentation approaches was carried out. Compared to manual segmentation, the authors' semiautomatic approach reduces the necessary user interaction by only requiring an indication of the anteroposterior orientation of the prostate and the selection of prostate center points on the apex, base, and midgland slices. Based on these inputs, the algorithm identifies candidate prostate boundary points using learned boundary appearance characteristics and performs regularization based on learned prostate shape information.

RESULTS

The semiautomated algorithm required an average of 30 s of user interaction time (measured for nine operators) for each 3D prostate segmentation. The authors compared the segmentations from this method to manual segmentations in a single-operator (mean whole gland MAD = 2.0 mm, DSC = 82%, recall = 77%, precision = 88%, and ΔV = - 4.6 cm(3)) and multioperator study (mean whole gland MAD = 2.2 mm, DSC = 77%, recall = 72%, precision = 86%, and ΔV = - 4.0 cm(3)). These results compared favorably with observed differences between manual segmentations and a simultaneous truth and performance level estimation reference for this data set (whole gland differences as high as MAD = 3.1 mm, DSC = 78%, recall = 66%, precision = 77%, and ΔV = 15.5 cm(3)). The authors found that overall, midgland segmentation was more accurate and repeatable than the segmentation of the apex and base, with the base posing the greatest challenge.

CONCLUSIONS

The main conclusions of this study were that (1) the semiautomated approach reduced interobserver segmentation variability; (2) the segmentation accuracy of the semiautomated approach, as well as the accuracies of recently published methods from other groups, were within the range of observed expert variability in manual prostate segmentation; and (3) further efforts in the development of computer-assisted segmentation would be most productive if focused on improvement of segmentation accuracy and reduction of variability within the prostatic apex and base.

Label image constrained multiatlas selection

Multiatlas based method is commonly used in medical image segmentation. In multiatlas based image segmentation, atlas selection and combination are considered as two key factors affecting the performance. Recently, manifold learning based atlas selection methods have emerged as very promising methods. However, due to the complexity of prostate structures in raw images, it is difficult to get accurate atlas selection results by only measuring the distance between raw images on the manifolds. Although the distance between the regions to be segmented across images can be readily obtained by the label images, it is infeasible to directly compute the distance between the test image (gray) and the label images (binary). This paper tries to address this problem by proposing a label image constrained atlas selection method, which exploits the label images to constrain the manifold projection of raw images. Analyzing the data point distribution of the selected atlases in the manifold subspace, a novel weight computation method for atlas combination is proposed. Compared with other related existing methods, the experimental results on prostate segmentation from T2w MRI showed that the selected atlases are closer to the target structure and more accurate segmentation were obtained by using our proposed method.

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.

Multi-resolution level sets with shape priors: a validation report for 2D segmentation of prostate gland in T2W MR images

The level set approach to segmentation of medical images has received considerable attention in recent years. Evolving an initial contour to converge to anatomical boundaries of an organ or tumor is a very appealing method, especially when it is based on a well-defined mathematical foundation. However, one drawback of such evolving method is its high computation time. It is desirable to design and implement algorithms that are not only accurate and robust but also fast in execution. Bresson et al. have proposed a variational model using both boundary and region information as well as shape priors. The latter can be a significant factor in medical image analysis. In this work, we combine the variational model of level set with a multi-resolution approach to accelerate the processing. The question is whether a multi-resolution context can make the segmentation faster without affecting the accuracy. As well, we investigate the question whether a premature convergence, which happens in a much shorter time, would reduce accuracy. We examine multiple semiautomated configurations to segment the prostate gland in T2W MR images. Comprehensive experimentation is conducted using a data set of a 100 patients (1,235 images) to verify the effectiveness of the multi-resolution level set with shape priors. The results show that the convergence speed can be increased by a factor of ≈ 2.5 without affecting the segmentation accuracy. Furthermore, a premature convergence approach drastically increases the segmentation speed by a factor of ≈ 17.9.

PCG-cut: graph driven segmentation of the prostate central gland

Prostate cancer is the most abundant cancer in men, with over 200,000 expected new cases and around 28,000 deaths in 2012 in the US alone. In this study, the segmentation results for the prostate central gland (PCG) in MR scans are presented. The aim of this research study is to apply a graph-based algorithm to automated segmentation (i.e. delineation) of organ limits for the prostate central gland. The ultimate goal is to apply automated segmentation approach to facilitate efficient MR-guided biopsy and radiation treatment planning. The automated segmentation algorithm used is graph-driven based on a spherical template. Therefore, rays are sent through the surface points of a polyhedron to sample the graph's nodes. After graph construction--which only requires the center of the polyhedron defined by the user and located inside the prostate center gland--the minimal cost closed set on the graph is computed via a polynomial time s-t-cut, which results in the segmentation of the prostate center gland's boundaries and volume. The algorithm has been realized as a C++ module within the medical research platform MeVisLab and the ground truth of the central gland boundaries were manually extracted by clinical experts (interventional radiologists) with several years of experience in prostate treatment. For evaluation the automated segmentations of the proposed scheme have been compared with the manual segmentations, yielding an average Dice Similarity Coefficient (DSC) of 78.94 ± 10.85%.

The use of an active appearance model for automated prostate segmentation in magnetic resonance

BACKGROUND

The prostate gland is delineated as the clinical target volume (CTV) in treatment planning of prostate cancer. Therefore, an accurate delineation is a prerequisite for efficient treatment. Accurate automated prostate segmentation methods facilitate the delineation of the CTV without inter-observer variation. The purpose of this study is to present an automated three-dimensional (3D) segmentation of the prostate using an active appearance model.

MATERIAL AND METHODS

Axial T2-weighted magnetic resonance (MR) scans were used to build the active appearance model. The model was based on a principal component analysis of shape and texture features with a level-set representation of the prostate shape instead of the selection of landmarks in the traditional active appearance model. To achieve a better fit of the model to the target image, prior knowledge to predict how to correct the model and pose parameters was incorporated. The segmentation was performed as an iterative algorithm to minimize the squared difference between the target and the model image.

RESULTS

The model was trained using manual delineations from 30 patients and was validated using leave-one-out cross validation where the automated segmentations were compared with the manual reference delineations. The mean and median dice similarity coefficient was 0.84 and 0.86, respectively.

CONCLUSION

This study demonstrated the feasibility for an automated prostate segmentation using an active appearance with results comparable to other studies.

Expert-driven label fusion in multi-atlas-based segmentation of the prostate using weighted atlases

PURPOSE

Automated segmentation is required for radiotherapy treatment planning, and multi-atlas methods are frequently used for this purpose. The combination of multiple intermediate results from multi-atlas segmentation into a single segmentation map can be achieved by label fusion. A method that includes expert knowledge in the label fusion phase of multi-atlas-based segmentation was developed. The method was tested by application to prostate segmentation, and the accuracy was compared to standard techniques.

METHODS

The selective and iterative method for performance level estimation (SIMPLE) algorithm for label fusion was modified with a weight map given by an expert that indicates the importance of each region in the evaluation of segmentation results. Voxel-based weights specified by an expert when performing the label fusion step in atlas-based segmentation were introduced into the modified SIMPLE algorithm. These weights incorporate expert knowledge on accuracy requirements in different regions of a segmentation. Using this knowledge, segmentation accuracy in regions known to be important can be improved by sacrificing segmentation accuracy in less important regions. Contextual information such as the presence of vulnerable tissue is then used in the segmentation process. This method using weight maps to fine-tune the result of multi-atlas-based segmentation was tested using a set of 146 atlas images consisting of an MR image of the lower abdomen and a prostate segmentation. Each image served as a target in a set of leave-one-out experiments. These experiments were repeated for a weight map derived from the clinical practice in our hospital.

RESULTS

The segmentation accuracy increased 6 % in regions that border vulnerable tissue using expert-specified voxel-based weight maps. This was achieved at the cost of a 4 % decrease in accuracy in less clinically relevant regions.

CONCLUSION

The inclusion of expert knowledge in a multi-atlas-based segmentation procedure was shown to be feasible for prostate segmentation. This method allows an expert to ensure that automatic segmentation is most accurate in critical regions. This improved local accuracy can increase the practical value of automatic segmentation.

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.

Multi-stage intra-patient template matching for prostate detection in MR volumes

Detecting objects, a significant task in computer vision, is accompanied with many challenges. When we focus on medical images, the challenges of detecting an organ or a tumour exhibit their own specific difficulties. Automatic finding of the prostate gland in magnetic resonance (MR) images, for instance, is a needed task in some clinical procedures. In this paper we propose a novel method for detection of the prostate gland in MR images. We use a multi-stage intra-patient approach to the template matching which is based on a trained dataset. We conduct experiments with the images of 100 patients and report preliminary results that seem to be promising.

A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images

Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented.

Patient specific prostate segmentation in 3-d magnetic resonance images

Accurate localization of the prostate and its surrounding tissue is essential in the treatment of prostate cancer. This paper presents a novel approach to fully automatically segment the prostate, including its seminal vesicles, within a few minutes of a magnetic resonance (MR) scan acquired without an endorectal coil. Such MR images are important in external beam radiation therapy, where using an endorectal coil is highly undesirable. The segmentation is obtained using a deformable model that is trained on-the-fly so that it is specific to the patient's scan. This case specific deformable model consists of a patient specific initialized triangulated surface and image feature model that are trained during its initialization. The image feature model is used to deform the initialized surface by template matching image features (via normalized cross-correlation) to the features of the scan. The resulting deformations are regularized over the surface via well established simple surface smoothing algorithms, which is then made anatomically valid via an optimized shape model. Mean and median Dice's similarity coefficients (DSCs) of 0.85 and 0.87 were achieved when segmenting 3T MR clinical scans of 50 patients. The median DSC result was equal to the inter-rater DSC and had a mean absolute surface error of 1.85 mm. The approach is showed to perform well near the apex and seminal vesicles of the prostate.

Multifeature landmark-free active appearance models: application to prostate MRI segmentation

Active shape models (ASMs) and active appearance models (AAMs) are popular approaches for medical image segmentation that use shape information to drive the segmentation process. Both approaches rely on image derived landmarks (specified either manually or automatically) to define the object's shape, which require accurate triangulation and alignment. An alternative approach to modeling shape is the levelset representation, defined as a set of signed distances to the object's surface. In addition, using multiple image derived attributes (IDAs) such as gradient information has previously shown to offer improved segmentation results when applied to ASMs, yet little work has been done exploring IDAs in the context of AAMs. In this work, we present a novel AAM methodology that utilizes the levelset implementation to overcome the issues relating to specifying landmarks, and locates the object of interest in a new image using a registration based scheme. Additionally, the framework allows for incorporation of multiple IDAs. Our multifeature landmark-free AAM (MFLAAM) utilizes an efficient, intuitive, and accurate algorithm for identifying those IDAs that will offer the most accurate segmentations. In this paper, we evaluate our MFLAAM scheme for the problem of prostate segmentation from T2-w MRI volumes. On a cohort of 108 studies, the levelset MFLAAM yielded a mean Dice accuracy of 88% ± 5%, and a mean surface error of 1.5 mm ±.8 mm with a segmentation time of 150/s per volume. In comparison, a state of the art AAM yielded mean Dice and surface error values of 86% ± 9% and 1.6 mm ± 1.0 mm, respectively. The differences with respect to our levelset-based MFLAAM model are statistically significant . In addition, our results were in most cases superior to several recent state of the art prostate MRI segmentation methods.

A feature-based learning framework for accurate prostate localization in CT images

Automatic segmentation of prostate in CT images plays an important role in medical image analysis and image guided radiation therapy. It remains as a challenging problem mainly due to three issues: First, the image contrast between the prostate and its surrounding tissues is low in prostate CT images and no obvious boundaries can be observed. Second, the unpredictable prostate motion causes large position variations of the prostate in the treatment images scanned at different treatment days. Third, the uncertainty of the existence of bowel gas in treatment images significantly changes the image appearance even for images taken from the same patient. To address these issues, in this paper we are motivated to propose a feature based learning framework for accurate prostate localization in CT images. The main contributions of the proposed method lie in the following aspects: (1) Anatomical features are extracted from input images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to help localize the prostate. (2) Regions with salient features but irrelevant to the localization of prostate, such as regions filled with bowel gas are automatically filtered out by the proposed method. (3) An online update mechanism is adopted in this paper to adaptively combine both population information and patient-specific information to localize the prostate. The proposed method is evaluated on a CT prostate dataset of 24 patients to localize the prostate, where each patient has more than 10 longitudinal images scanned at different treatment times. It is also compared with several state-of- the-art prostate localization algorithms in CT images, and the experimental results demonstrate that the proposed method achieves the highest localization accuracy among all the methods under comparison.

Medical image registration: a review

This paper presents a review of automated image registration methodologies that have been used in the medical field. The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application. The registration methodologies under review are classified into intensity or feature based. The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described.

An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy

PURPOSE

Prostate radiation therapy dose planning directly on magnetic resonance imaging (MRI) scans would reduce costs and uncertainties due to multimodality image registration. Adaptive planning using a combined MRI-linear accelerator approach will also require dose calculations to be performed using MRI data. The aim of this work was to develop an atlas-based method to map realistic electron densities to MRI scans for dose calculations and digitally reconstructed radiograph (DRR) generation.

METHODS AND MATERIALS

Whole-pelvis MRI and CT scan data were collected from 39 prostate patients. Scans from 2 patients showed significantly different anatomy from that of the remaining patient population, and these patients were excluded. A whole-pelvis MRI atlas was generated based on the manually delineated MRI scans. In addition, a conjugate electron-density atlas was generated from the coregistered computed tomography (CT)-MRI scans. Pseudo-CT scans for each patient were automatically generated by global and nonrigid registration of the MRI atlas to the patient MRI scan, followed by application of the same transformations to the electron-density atlas. Comparisons were made between organ segmentations by using the Dice similarity coefficient (DSC) and point dose calculations for 26 patients on planning CT and pseudo-CT scans.

RESULTS

The agreement between pseudo-CT and planning CT was quantified by differences in the point dose at isocenter and distance to agreement in corresponding voxels. Dose differences were found to be less than 2%. Chi-squared values indicated that the planning CT and pseudo-CT dose distributions were equivalent. No significant differences (p > 0.9) were found between CT and pseudo-CT Hounsfield units for organs of interest. Mean ± standard deviation DSC scores for the atlas-based segmentation of the pelvic bones were 0.79 ± 0.12, 0.70 ± 0.14 for the prostate, 0.64 ± 0.16 for the bladder, and 0.63 ± 0.16 for the rectum.

CONCLUSIONS

The electron-density atlas method provides the ability to automatically define organs and map realistic electron densities to MRI scans for radiotherapy dose planning and DRR generation. This method provides the necessary tools for MRI-alone treatment planning and adaptive MRI-based prostate radiation therapy.