Prostate MR image segmentation using 3D active appearance models

This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain the point correspondence between the given training cases. Subsequently, an AAM is used to segment the prostate on 50 training cases. The method is evaluated using a 5-fold cross validation over 5 repetitions. The mean Dice similarity coefficient and 95% Hausdorff distance are 0.78 and 7.32 mm respectively

Comparison of dynamic susceptibility contrast-MRI perfusion quantification methods in the presence of delay and dispersion

The perfusion of the brain is essential to maintain brain function. Stroke is an example of a decrease in blood flow and reduced perfusion. During ischemic stroke the blood flow to tissue is hampered due to a clot inside a vessel. To investigate the recovery of stroke patients, follow up studies are necessary. MRI is the preferred imaging modality for follow up because of the absence of radiation dose concerns, contrary to CT. Dynamic Susceptibility Contrast (DSC) MRI is an imaging technique used for measuring perfusion of the brain, however, is not standard applied in the clinical routine due to lack of immediate patient benefit. Several post processing algorithms are described in the literature to obtain cerebral blood flow (CBF). The quantification of CBF relies on the deconvolution of a tracer concentration-time curve in an arterial and a tissue voxel. There are several methods to obtain this deconvolution based on singular-value decomposition (SVD). This contribution describes a comparison between the different approaches as currently there is no best practice for (all) clinical relevant situations. We investigate the influence of tracer delay, dispersion and recirculation on the performance of the methods. In the presence of negative delays, the truncated SVD approach overestimates the CBF. Block-circulant and reformulated SVD are delay-independent. Due to its delay dependent behavior, the truncated SVD approach performs worse in the presence of dispersion as well. However all SVD approaches are dependent on the amount of dispersion. Moreover, we observe that the optimal truncation parameter varies when recirculation is added to noisy data, suggesting that, in practice, these methods are not immune to tracer recirculation. Finally, applying the methods to clinical data resulted in a large variability of the CBF estimates. Block-circulant SVD will work in all situations and is the method with the highest potential

Comparison of dynamic susceptibility contrast-MRI perfusion quantification methods in the presence of delay and dispersion

The perfusion of the brain is essential to maintain brain function. Stroke is an example of a decrease in blood flow and reduced perfusion. During ischemic stroke the blood flow to tissue is hampered due to a clot inside a vessel. To investigate the recovery of stroke patients, follow up studies are necessary. MRI is the preferred imaging modality for follow up because of the absence of radiation dose concerns, contrary to CT. Dynamic Susceptibility Contrast (DSC) MRI is an imaging technique used for measuring perfusion of the brain, however, is not standard applied in the clinical routine due to lack of immediate patient benefit. Several post processing algorithms are described in the literature to obtain cerebral blood flow (CBF). The quantification of CBF relies on the deconvolution of a tracer concentration-time curve in an arterial and a tissue voxel. There are several methods to obtain this deconvolution based on singular-value decomposition (SVD). This contribution describes a comparison between the different approaches as currently there is no best practice for (all) clinical relevant situations. We investigate the influence of tracer delay, dispersion and recirculation on the performance of the methods. In the presence of negative delays, the truncated SVD approach overestimates the CBF. Block-circulant and reformulated SVD are delay-independent. Due to its delay dependent behavior, the truncated SVD approach performs worse in the presence of dispersion as well. However all SVD approaches are dependent on the amount of dispersion. Moreover, we observe that the optimal truncation parameter varies when recirculation is added to noisy data, suggesting that, in practice, these methods are not immune to tracer recirculation. Finally, applying the methods to clinical data resulted in a large variability of the CBF estimates. Block-circulant SVD will work in all situations and is the method with the highest potential

Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05p<0.05) and had an efficient implementation with a run time of 8 min and 3 s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/

Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05p<0.05) and had an efficient implementation with a run time of 8 min and 3 s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/