Prostate Core

Clare Tempany Kemal Tuncali Fiona Fennessy Junichi Tokuda Andrey Fedorov
Clare M. Tempany, MD
Core Lead
Kemal Tuncali, MD
Co-Investigator
Fiona Fennessy, MD, PhD
Project Lead
Junichi Tokuda, PhD
Project Lead
Andriy Fedorov, PhD
Project Lead

There are two complex issues that drive the clinical need to change current paradigms for prostate cancer (PCa): The inability to predict aggressiveness of a given cancer, which in turn leads to over treatment, and the increasing evidence that disease progression in men with seemingly low-risk PCa is due to inadequate biopsy sampling. Recent trends indicate that the treatment of patients with localized PCa is shifting more and more towards either active surveillance or focal therapy. Technical solutions to address these challenges, and their validation in clinic, are lacking. We are working to address these challenges by integrating innovative MR image acquisition and analysis with the MR targeted biopsy platform we developed in the previous cycle. We are developing a diagnostic biomedical imaging platform to detect, characterize and diagnose prostate cancer and will provide new opportunities to understand the aggressiveness and heterogeneity of prostate cancer and ultimately allow for development and testing of new predictive markers in focal therapy. Our projects are:

Platform for validating novel imaging biomarkers with molecular and routine pathology.  We are developing methods of assessment of tumor heterogeneity by supplementing mpMRI with new hypoxia and multi b value MR imaging and add molecular profiling to the pathology options for core biopsy tissue, thus provide a unique platform for imaging, biopsy and both routine and molecular pathology. We will correlate genomic diversity with MR imaging parameters. We will propose novel motion compensation techniques combined with hypoxia imaging that will be applied jointly with the multi-b-value diffusion weighted imaging (DWI) for improved characterization of PCa. These novel-imaging approaches will be validated in biopsy and cryotherapy patient cohorts. (Contact: Clare M. Tempany, Fiona Fennessy)

Platform for focal cryoablation of PCa with accurate temperature mapping and motion compensation. Our goal is to develop and evaluate thermometry methods to monitor MR-guided focal cryoablation for localized prostate cancer, by internal ice ball thermometry using a “voxelwise thermal history” method. We will develop and test a new method for tracking the prostate gland motion, using active MR tracking coils embedded in a urethral warming catheter, investigate Ultrashort TE (UTE) MRI to monitor the internal thermal dosimetry within the ice-ball, and develop and evaluate software for voxel-wise thermal history tracking during all stages of the procedure. (Contact: Junichi Tokuda)

Informatics solution in support of targeted prostate biopsy and focal therapy for localized prostate cancer. This aim will have three tasks: 1) develop software tools to support structured PCa reporting and image registration for biopsy and focal therapy applications; 2) investigate and implement improved practices for structured data collection and provenance, prepare and disseminate curated validation datasets to facilitate validation of the role of mpMRI in cancer characterization and the evaluation of image registration accuracy/reliability; 3) investigate methods for non-rigid registration to enable recovery of prostate gland deformation for treatment response assessment and propose and apply methodologies for statistical assessment of the reliability of the registration tools. All three projects are interconnected, and leverage unique resources provided by this Center. In addition to developing novel technologies, we are creating a platform for collecting validation imaging datasets annotated with the analysis results, molecular and pathology markers, to build a unique resource for investigating the role of imaging and development of novel image analysis tools for prostate cancer. (Contact: Andriy Fedorov)

Software and Documentation

3D Slicer, a comprehensive open source platform for medical image analysis, contains several modules that have been contributed by us for Image-Guided Prostate Interventions. These include:

Data

Presentations

These presentations have been selected as tutorials for readers interested in learning about the clinical science and technology of the Prostate Core.

Links

Full Publication List

In NIH/NLM database and in our Abstracts Database

Select Recent Publications

Fiona M Fennessy, Andriy Fedorov, Mark G Vangel, Robert V. Mulkern, Maria Tretiakova, Rosina T Lis, Clare Tempany, and Mary-Ellen Taplin. 10/2020. “Multiparametric MRI as a Biomarker of Response to Neoadjuvant Therapy for Localized Prostate Cancer-A Pilot Study.” Acad Radiol, 27, 10, Pp. 1432-9.Abstract
RATIONALE AND OBJECTIVES: To explore a role for multiparametric MRI (mpMRI) as a biomarker of response to neoadjuvant androgen deprivation therapy (ADT) for prostate cancer (PCa). MATERIALS AND METHODS: This prospective study was approved by the institutional review board and was HIPAA compliant. Eight patients with localized PCa had a baseline mpMRI, repeated after 6-months of ADT, followed by prostatectomy. mpMRI indices were extracted from tumor and normal regions of interest (TROI/NROI). Residual cancer burden (RCB) was measured on mpMRI and on the prostatectomy specimen. Paired t-tests compared TROI/NROI mpMRI indices and pre/post-treatment TROI mpMRI indices. Spearman's rank tested for correlations between MRI/pathology-based RCB, and between pathological RCB and mpMRI indices. RESULTS: At baseline, TROI apparent diffusion coefficient (ADC) was lower and dynamic contrast enhanced (DCE) metrics were higher, compared to NROI (ADC: 806 ± 137 × 10 vs. 1277 ± 213 × 10 mm/sec, p = 0.0005; K: 0.346 ± 0.16 vs. 0.144 ± 0.06 min, p = 0.002; AUC: 0.213 ± 0.08 vs. 0.11 ± 0.03, p = 0.002). Post-treatment, there was no change in TROI ADC, but a decrease in TROI K (0.346 ± 0.16 to 0.188 ± 0.08 min; p = 0.02) and AUC (0.213 ± 0.08 to 0.13 ± 0.06; p = 0.02). Tumor volume decreased with ADT. There was no difference between mpMRI-based and pathology-based RCB, which positively correlated (⍴ = 0.74-0.81, p < 0.05). Pathology-based RCB positively correlated with post-treatment DCE metrics (⍴ = 0.76-0.70, p < 0.05) and negatively with ADC (⍴ = -0.79, p = 0.03). CONCLUSION: Given the heterogeneity of PCa, an individualized approach to ADT may maximize potential benefit. This pilot study suggests that mpMRI may serve as a biomarker of ADT response and as a surrogate for RCB at prostatectomy.
Atsushi Yamada, Junichi Tokuda, Shigeyuki Naka, Koichiro Murakami, Tohru Tani, and Shigehiro Morikawa. 3/2020. “Magnetic Resonance and Ultrasound Image-guided Navigation System using a Needle Manipulator.” Med Phys, 47, 3, Pp. 850-8.Abstract
PURPOSE: Image guidance is crucial for percutaneous tumor ablations, enabling accurate needle-like applicator placement into target tumors while avoiding tissues that are sensitive to injury and/or correcting needle deflection. Although ultrasound (US) is widely used for image guidance, magnetic resonance (MR) is preferable due to its superior soft tissue contrast. The objective of this study was to develop and evaluate an MR and US multi-modal image-guided navigation system with a needle manipulator to enable US-guided applicator placement during MR imaging (MRI)-guided percutaneous tumor ablation. METHODS: The MRI-compatible needle manipulator with US probe was installed adjacent to a 3 Tesla MRI scanner patient table. Coordinate systems for the MR image, patient table, manipulator, and US probe were all registered using an optical tracking sensor. The patient was initially scanned in the MRI scanner bore for planning and then moved outside the bore for treatment. Needle insertion was guided by real-time US imaging fused with the reformatted static MR image to enhance soft tissue contrast. Feasibility, targeting accuracy, and MR compatibility of the system were evaluated using a bovine liver and agar phantoms. RESULTS: Targeting error for 50 needle insertions was 1.6 ± 0.6 mm (mean ± standard deviation). The experiment confirmed that fused MR and US images provided real-time needle localization against static MR images with soft tissue contrast. CONCLUSIONS: The proposed MR and US multi-modal image-guided navigation system using a needle manipulator enabled accurate needle insertion by taking advantage of static MR and real-time US images simultaneously. Real-time visualization helped determine needle depth, tissue monitoring surrounding the needle path, target organ shifts, and needle deviation from the path.
Christian Herz, Kyle MacNeil, Peter A Behringer, Junichi Tokuda, Alireza Mehrtash, Parvin Mousavi, Ron Kikinis, Fiona M Fennessy, Clare M Tempany, Kemal Tuncali, and Andriy Fedorov. 2/2020. “Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy.” IEEE Trans Biomed Eng, 67, 2, Pp. 565-76.Abstract
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.
Ananya Panda, Verena C Obmann, Wei-Ching Lo, Seunghee Margevicius, Yun Jiang, Mark Schluchter, Indravadan J Patel, Dean Nakamoto, Chaitra Badve, Mark A Griswold, Irina Jaeger, Lee E Ponsky, and Vikas Gulani. 2019. “MR Fingerprinting and ADC Mapping for Characterization of Lesions in the Transition Zone of the Prostate Gland.” Radiology, 292, 3, Pp. 685-94.Abstract
BackgroundPreliminary studies have shown that MR fingerprinting-based relaxometry combined with apparent diffusion coefficient (ADC) mapping can be used to differentiate normal peripheral zone from prostate cancer and prostatitis. The utility of relaxometry and ADC mapping for the transition zone (TZ) is unknown.PurposeTo evaluate the utility of MR fingerprinting combined with ADC mapping for characterizing TZ lesions.Materials and MethodsTZ lesions that were suspicious for cancer in men who underwent MRI with T2-weighted imaging and ADC mapping ( values, 50-1400 sec/mm), MR fingerprinting with steady-state free precession, and targeted biopsy (60 in-gantry and 15 cognitive targeting) between September 2014 and August 2018 in a single university hospital were retrospectively analyzed. Two radiologists blinded to Prostate Imaging Reporting and Data System (PI-RADS) scores and pathologic diagnosis drew regions of interest on cancer-suspicious lesions and contralateral visually normal TZs (NTZs) on MR fingerprinting and ADC maps. Linear mixed models compared two-reader means of T1, T2, and ADC. Generalized estimating equations logistic regression analysis was used to evaluate both MR fingerprinting and ADC in differentiating NTZ, cancers and noncancers, clinically significant (Gleason score ≥ 7) cancers from clinically insignificant lesions (noncancers and Gleason 6 cancers), and characterizing PI-RADS version 2 category 3 lesions.ResultsIn 67 men (mean age, 66 years ± 8 [standard deviation]) with 75 lesions, targeted biopsy revealed 37 cancers (six PI-RADS category 3 cancers and 31 PI-RADS category 4 or 5 cancers) and 38 noncancers (31 PI-RADS category 3 lesions and seven PI-RADS category 4 or 5 lesions). The T1, T2, and ADC of NTZ (1800 msec ± 150, 65 msec ± 22, and [1.13 ± 0.19] × 10 mm/sec, respectively) were higher than those in cancers (1450 msec ± 110, 36 msec ± 11, and [0.57 ± 0.13] × 10 mm/sec, respectively; < .001 for all). The T1, T2, and ADC in cancers were lower than those in noncancers (1620 msec ± 120, 47 msec ± 16, and [0.82 ± 0.13] × 10 mm/sec, respectively; = .001 for T1 and ADC and = .03 for T2). The area under the receiver operating characteristic curve (AUC) for T1 plus ADC was 0.94 for separation. T1 and ADC in clinically significant cancers (1440 msec ± 140 and [0.58 ± 0.14] × 10 mm/sec, respectively) were lower than those in clinically insignificant lesions (1580 msec ± 120 and [0.75 ± 0.17] × 10 mm/sec, respectively; = .001 for all). The AUC for T1 plus ADC was 0.81 for separation. Within PI-RADS category 3 lesions, T1 and ADC of cancers (1430 msec ± 220 and [0.60 ± 0.17] × 10 mm/sec, respectively) were lower than those of noncancers (1630 msec ± 120 and [0.81 ± 0.13] × 10 mm/sec, respectively; = .006 for T1 and = .004 for ADC). The AUC for T1 was 0.79 for differentiating category 3 lesions.ConclusionMR fingerprinting-based relaxometry combined with apparent diffusion coefficient mapping may improve transition zone lesion characterization.© RSNA, 2019
Wei Huang, Yiyi Chen, Andriy Fedorov, Xia Li, Guido H Jajamovich, Dariya I Malyarenko, Madhava P Aryal, Peter S LaViolette, Matthew J Oborski, Finbarr O'Sullivan, Richard G Abramson, Kourosh Jafari-Khouzani, Aneela Afzal, Alina Tudorica, Brendan Moloney, Sandeep N Gupta, Cecilia Besa, Jayashree Kalpathy-Cramer, James M Mountz, Charles M Laymon, Mark Muzi, Paul E Kinahan, Kathleen Schmainda, Yue Cao, Thomas L Chenevert, Bachir Taouli, Thomas E Yankeelov, Fiona Fennessy, and Xin Li. 2019. “The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.” Tomography, 5, 1, Pp. 99-109.Abstract
This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate K (volume transfer rate constant), v (extravascular, extracellular volume fraction), k (efflux rate constant), and τ (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for K, v, k, and τ, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in K and v (wCV = 0.50 and 0.10, respectively), but had smaller effects on k and τ (wCV = 0.39 and 0.22, respectively). k is less sensitive to AIF variation than K, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τ parameter may have advantages over the conventional PK parameters in a longitudinal study.
Wenya Linda Bi, Ahmed Hosny, Matthew B Schabath, Maryellen L Giger, Nicolai J Birkbak, Alireza Mehrtash, Tavis Allison, Omar Arnaout, Christopher Abbosh, Ian F Dunn, Raymond H Mak, Rulla M Tamimi, Clare M Tempany, Charles Swanton, Udo Hoffmann, Lawrence H Schwartz, Robert J Gillies, Raymond Y Huang, and Hugo JWL Aerts. 2019. “Artificial Intelligence in Cancer Imaging: Clinical Challenges and Applications.” CA Cancer J Clin, 69, 2, Pp. 127-57.Abstract
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
Elizabeth C Randall, Giorgia Zadra, Paolo Chetta, Begona GC Lopez, Sudeepa Syamala, Sankha S Basu, Jeffrey N Agar, Massimo Loda, Clare M Tempany, Fiona M Fennessy, and Nathalie YR Agar. 2019. “Molecular Characterization of Prostate Cancer with Associated Gleason Score using Mass Spectrometry Imaging.” Mol Cancer Res, 17, 5, Pp. 1155-65.Abstract
Diagnosis of prostate cancer is based on histological evaluation of tumor architecture using a system known as the 'Gleason score'. This diagnostic paradigm, while the standard of care, is time-consuming, shows intra-observer variability and provides no information about the altered metabolic pathways, which result in altered tissue architecture. Characterization of the molecular composition of prostate cancer and how it changes with respect to the Gleason score (GS) could enable a more objective and faster diagnosis. It may also aid in our understanding of disease onset and progression. In this work, we present mass spectrometry imaging for identification and mapping of lipids and metabolites in prostate tissue from patients with known prostate cancer with GS from 6 to 9. A gradient of changes in the intensity of various lipids was observed, which correlated with increasing GS. Interestingly, these changes were identified in both regions of high tumor cell density, and in regions of tissue that appeared histologically benign, possibly suggestive of pre-cancerous metabolomic changes. A total of 31 lipids, including several phosphatidylcholines, phosphatidic acids, phosphatidylserines, phosphatidylinositols and cardiolipins were detected with higher intensity in GS (4+3) compared with GS (3+4), suggesting they may be markers of prostate cancer aggression. Results obtained through mass spectrometry imaging studies were subsequently correlated with a fast, ambient mass spectrometry method for potential use as a clinical tool to support image-guided prostate biopsy. Implications: In this study we suggest that metabolomic differences between prostate cancers with different Gleason scores can be detected by mass spectrometry imaging.
Pelin Aksit Ciris, Jr-yuan George Chiou, Daniel I Glazer, Tzu-Cheng Chao, Clare M Tempany-Afdhal, Bruno Madore, and Stephan E. Maier. 2019. “Accelerated Segmented Diffusion-Weighted Prostate Imaging for Higher Resolution, Higher Geometric Fidelity, and Multi-b Perfusion Estimation.” Invest Radiol, 54, 4, Pp. 238-46.Abstract
PURPOSE: The aim of this study was to improve the geometric fidelity and spatial resolution of multi-b diffusion-weighted magnetic resonance imaging of the prostate. MATERIALS AND METHODS: An accelerated segmented diffusion imaging sequence was developed and evaluated in 25 patients undergoing multiparametric magnetic resonance imaging examinations of the prostate. A reduced field of view was acquired using an endorectal coil. The number of sampled diffusion weightings, or b-factors, was increased to allow estimation of tissue perfusion based on the intravoxel incoherent motion (IVIM) model. Apparent diffusion coefficients measured with the proposed segmented method were compared with those obtained with conventional single-shot echo-planar imaging (EPI). RESULTS: Compared with single-shot EPI, the segmented method resulted in faster acquisition with 2-fold improvement in spatial resolution and a greater than 3-fold improvement in geometric fidelity. Apparent diffusion coefficient values measured with the novel sequence demonstrated excellent agreement with those obtained from the conventional scan (R = 0.91 for bmax = 500 s/mm and R = 0.89 for bmax = 1400 s/mm). The IVIM perfusion fraction was 4.0% ± 2.7% for normal peripheral zone, 6.6% ± 3.6% for normal transition zone, and 4.4% ± 2.9% for suspected tumor lesions. CONCLUSIONS: The proposed accelerated segmented prostate diffusion imaging sequence achieved improvements in both spatial resolution and geometric fidelity, along with concurrent quantification of IVIM perfusion.
Sharon Peled, Mark Vangel, Ron Kikinis, Clare M Tempany, Fiona M Fennessy, and Andrey Fedorov. 2019. “Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI.” Acad Radiol, 26, 9, Pp. e241-e251.Abstract
RATIONALE AND OBJECTIVES: Analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging is notable for the variability of calculated parameters. The purpose of this study was to evaluate the level of measurement variability and error/variability due to modeling in DCE magnetic resonance imaging parameters. MATERIALS AND METHODS: Two prostate DCE scans were performed on 11 treatment-naïve patients with suspected or confirmed prostate peripheral zone cancer within an interval of less than two weeks. Tumor-suspicious and normal-appearing regions of interest (ROI) in the prostate peripheral zone were segmented. Different Tofts-Kety based models and different arterial input functions, with and without bolus arrival time (BAT) correction, were used to extract pharmacokinetic parameters. The percent repeatability coefficient (%RC) of fitted model parameters K, v, and k was calculated. Paired t-tests comparing parameters in tumor-suspicious ROIs and in normal-appearing tissue evaluated each parameter's sensitivity to pathology. RESULTS: Although goodness-of-fit criteria favored the four-parameter extended Tofts-Kety model with the BAT correction included, the simplest two-parameter Tofts-Kety model overall yielded the best repeatability scores. The best %RC in the tumor-suspicious ROI was 63% for k, 28% for v and 83% for K . The best p values for discrimination between tissues were p <10 for k and K, and p = 0.11 for v. Addition of the BAT correction to the models did not improve repeatability. CONCLUSION: The parameter k, using an arterial input functions directly measured from blood signals, was more repeatable than K. Both K and k values were highly discriminatory between healthy and diseased tissues in all cases. The parameter v had high repeatability but could not distinguish the two tissue types.
Alireza Mehrtash, Mohsen Ghafoorian, Guillaume Pernelle, Alireza Ziaei, Friso G Heslinga, Kemal Tuncali, Andriy Fedorov, Ron Kikinis, Clare M Tempany, William M Wells, Purang Abolmaesumi, and Tina Kapur. 2019. “Automatic Needle Segmentation and Localization in MRI with 3D Convolutional Neural Networks: Application to MRI-targeted Prostate Biopsy.” IEEE Trans Med Imaging., 38, 4, Pp. 1026-36.Abstract
Image-guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application, and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperenial MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-dimensional fully convolutional neural network model was developed, trained and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80 mm average in needle tip detection, and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared to the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.