Prostate cancer caused necrosis segmentation and registration system for perfusion analysis via ethrive imaging

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1 Prostate cancer caused necrosis segmentation and registration system for perfusion analysis via ethrive imaging Jacky Ko Ka Long 1,2,3, Wang Defeng 1,2,3, Simon Yu Chun Ho 1 1 Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong @link.cuhk.edu.hk, {dfwang, simonyu}@cuhk.edu.hk 2 Research Centre for Medical Image Computing, The Chinese University of Hong Kong, Hong Kong 3 Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China Abstract: The biological and clinical heterogeneous features of prostate cancers makes the imaging evaluation a difficult task. The well-defined Prostate Imaging and Data System have issued a new clinical diagnosis standard since For the convenience of continuous prostate MRI monitoring system, a rapid prostate tissue identification and quantification system was in high demand. In this study, necrosis caused by prostate cancers was identified by ethrive imaging with a semi-automatic segmentation and registration pipeline. Tempospatial alignment was applied over longitudinal scanned data to overcome tissue deformation problem with manual registration initialization. The result showed significant difference in DCE value, in specific maximum enhancement, at different prostate tissues. Keywords: prostate cancer, dynamic contrast enhance, necrosis, segmentation, ethrive 1 Introduction There is one sixth of chance for a men to be diagnosed with prostate cancer in his lifetime.[1], [2] Accounting for approximately 360,000 and 180,000 annual new cases in Europe and America respectively, the disease has become the second lead cause of cancer death among Westerners.[3] Prostate cancer can be a serious disease, but with sufficient clinical monitoring, the 15 years survival rate could be up to 95%.[4] However, the biological and clinical heterogeneous features of prostate cancers makes imaging evaluation a challenging task. Application of different imaging modalities allowed the clinicians to diagnosis, characterize and localize the tumor regions. Ultrasound (US) and magnetic resonance imaging (MRI) are the two main imaging techniques applied in the cancer detection. The US imaging is widely used for biopsy guidance and sometimes hypoechoic area is observed.[5] Indeed, the poor tissue resolution of US limited its clinical diagnosis values. MRI, with a high spatial resolution in soft tissue imaging, has shown its advantages in disease stage monitoring. Multiparametric magnetic resonance imaging accurately detect prostate cancer even when transrectal ultrasound biopsy does not show malignancy.[6], [7] Prostate Imaging-Reporting and Data System (PI-RADS) is a well-defined clinical standard for multi-parametric MRI.[6] [8] The system which includes the image acquisition and reporting was developed by the European Society of Urogenital Radiology (ESUR) in

2 2012. Individual PI-RADS scores are given on every lesion inside the prostate. The 1-5 scaling scoring system was measured by T2 weighted imaging (T2W), diffusion weighted imaging (DWI), magnetic resonance spectroscopy (MRS) and dynamic contrast enhancement (DCE). The higher rated score indicates a higher probability in malign. Based on recent studies, the aggregated PI-RADS score 10 points with T2W, DWI and DCE are widely used with a highly predictive diagnostic marker for malignancy. With MRS available, total malignancy threshold would set at 13 points level. The American college of Radiology (ACR) and ESUR released the updated version of PI-RADS (PI-RADS v2) in late 2014.[9] The new assessment standard introduced the concept of dominant sequence: T2W for transition zone lesions and DWI for peripheral zone lesions. MRS is not a part the new scoring system. Though the very recent update in PI-RADS, the differentiation between normal tissues, prostatitis, tumor tissue and necrosis tissue is difficult. The hypervascularized region with high perfusion activity showed hyperintensity under three dimensional T1 high-resolution isovolumetric examination (ethrive) after gadolinium injection, while the poor blood supplied necrosis regions has a hypointensity with same imaging protocol.[10] The investigation in longitudinal monitor of prostate necrosis regions is rare. The quantification faces several technical difficulties such as necrosis tissue segmentation, and the tempospatial deformation. In this study, we suggest a semi-automatic prostate registration and segmentation pipeline. A minimal human involvement is designed to reduce subjective factors and analysis time. Necrosis tissue DCE values are also measured as one of the key scoring factors in PI-RADS. 2 Methods 2.1 Patient selection The study protocol was approved by the Ethics Committee and written consent was obtained from all patients participated in this study. Eligible patients diagnosed with significant prostate tumor volume were recruited for a longitudinal MR scanning. A total of 11 patients were recruited to participate in baseline scanning. Follow up scanning were done after 2 weeks and 6 months. 2.2 Image acquisition MRI examination were performed on a 3.0T whole body scanner (Achieva, Philips Medical Systems, Best, The Netherlands). High resolution three dimensional T1 high-resolution isovolumetric examination (ethrive) and dynamic contrast enhancement (DCE) images were acquired in coronal plane. An additional T2 weighted image is scanned in axial and sagittal plane for the evaluation of disease. ethrive with gadolinium contrast injection images were obtained with echo time TE = 1.85ms and repetition time TR = 3.76ms respectively. Slice thickness was set at 2 mm with voxel size mm x mm. Flipping angle was set at 10

3 DCE imaging was performed with an axial 3D T1 spoiled gradient echo sequence (TE = 1.96 ms; TR = 3.81ms; image matrix dimensions = 288 x 288 x 40; voxel size = 0.97mm x 0.97 mm x 2mm; flip angle = 10 ). 13 images were acquired for every 7 seconds. 2.3 Preprocessing Image registration and resampling Acquired MR images were outputted as DICOM format then converted into 3D NIFTI format. A manual transformation was applied on baseline and 6 weeks ethrive images for fast alignment to the 2 weeks ethrive image as an initialization for fine image registration. Rigid image registrations with maximizing mutual information was performed by Expert Automated Registration (National Alliance for Medical Image Computing, NA-MIC).[11] The 2 weeks ethrive data was set as fixed images. The initialization manual transform and registration transform was combined to single transformation matrix then applied to the non-registered ethrive and DCE data obtained from baseline and 6 weeks follow up scanning. All data were resampled to space of 2 weeks ethrive images by linear interpolation. Necrosis segmentation Overall volume of prostate gland and urinal bladder was assessed by manually drawing region of interest around the organs on each ethrive slice. The segmentation procedure was implemented by contour widget of Visualization Toolkit (VTK). Segmentation labels over prostate was then forward to Image and Segmentation Toolkit (ITK) image format as the prostate label mask. The masked prostate region was separated into two distinctive regions, namely normal tissues and necrosis tissues, by Otsu multi thresholding method.[12] The algorithm was a long established technique to estimate valley points in the image histogram. Number of histogram bins was set at 256 and number of thresholds was set at 3, including the background as a clear label. Multi-threading was also applied for a rapid calulation in runtime interaction. The detailed preprocessing pipeline was shown in Figure 1.

4 Fig. 1. Flow chart for the semi-automatic registration and segmentation scheme on the prostate perfusion data. 2.4 Perfusion analysis The typical perfusion curve gradually increase from base intensity value then to a saturation point when intravenous contrast agent was injected. The averaged intensity values at normal prostate tissue and necrosis tissue were measured over 13 imaging time points in a 7 second time step. Curve fitting algorithm was implemented from Scipy optimization toolkit with following equation: I( t) A arctan(k t c) I 0 (1) Where A was the enhancement amplitude, k was the time scaling factor, c was the time shifting factor, and I 0 was the perfusion value before enhancement. Two indices were measure according to perfusion curve obtained from equation (1), namely maximum enhancement (ME) and enhancement slope (ES).[13] ME was defined

5 as the maximum percentage increase in signal intensity from base I base to maximum I max. ES was defined as the rate of enhancement between 10% and 90% of the slope between and. The formulae were given by: I max I base Where t 10% and 90% of difference between shown in Figure 2. ME ( I I ) / I 100% (2) max base base ES ( I I ) / ( t t ) 100% (3) 90% 10% 90% 10% t 90% were the time intervals when signal intensity reaches 10% and I base and I max, respectively. An example fitted curve was Fig 2. 3T Time-intensity perfusion curve and corresponding ME and ES for a typical patient. The two red dots represent the time points when signal intensity reaches 10% and 90% of the maximum signal intensity difference between base 3 Results and Discussion I I and max respectively. Segmentation With good image contrast at prostate region, the result of necrosis region segmentation was satisfactory with Otsu multiple thresholding method. Small scattered

6 points were identified as necrosis at peripherals. (Fig. 3) These regions were relatively in small volume in comparison with the solid necrosis regions. The less vascularized tissues or localized uneven capillary distribution may be the possible reasons to hypointensity at prostate peripherals under ethrive imaging. Fig. 3. Result of semi-automatic prostate necrosis region segmentation. Green: Normal tissue, Red: Necrosis Perfusion Analysis Mean values of manually segmented prostate and urinal bladder volume were listed in Table 1. A significant reduction in prostate volume was observed over the half year follow up. Moreover, mean DCE perfusion parameters ME and ES were also listed. A gradually reduction in ME value on normal prostate regions refers to less vascularized blood supply to the organ. The suppressed ME would be an indication for recovering from prostate cancer or prostatitis. Perfusion performance over prostate necrosis region was the main focus of our study. A significant lower ME was observed in comparison with the one in non-necrosis regions. A low blood supply revealed that the poor vascularized tissue identified by ethrive scanning was in good agreement in DCE images. The increment in ME at necrosis from 2 weeks to 6 months could be interpreted as the tissue re-vascularization or recovery. For an interactive presentation of our developed software, we intend to have a live demonstration in 2016 MICCAI IMIC workshop.

7 Table 1. Prostate cancer DCE indices. Time Point Prostate Volume Bladder Volume Normal ME Normal ES Necrosis ME Necrosis ES baseline weeks months Necrosis Volume Normal Prostate Volume Conclusion Multiparametric MRI has shown its clinical significance since the release of PI-RADS. Our research suggested a rapid monitoring system with tempospatial image alignments. Tissue contents of the diseased prostate could be rapidly identified with our semi-automatic pipeline. The interactive monitoring system observed a significant difference in DCE maximum enhancement between necrosis and other prostate tissues. This would suggest an insight for subjective quantification value in future prostate cancer scoring system. 5 Acknowledgement The work described in this paper was partially supported by grants from the Innovation and Technology Commission (Project No: GHP/028/14SZ, ITS/293/14FP), a grant from The Science, Technology and Innovation Commission of Shenzhen Municipality (Project No.: CXZZ ), a grant from Technology and Business Development Fund (Project No.: TBF15MED004), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No.: CUHK ), and a grant from the National Natural Science Foundation of China (Project No ) 6 Reference [1] J. O. Barentsz, J. Richenberg, R. Clements, P. Choyke, S. Verma, G. Villeirs, O. Rouviere, V. Logager, and J. J. Fütterer, ESUR prostate MR guidelines 2012, Eur. Radiol., vol. 22, no. 4, pp , Feb [2] GLOBOCAN Cancer Fact Sheets: prostate cancer. [Online]. Available: [Accessed: 08-Jul- 2016]. [3] American Cancer Society, American Cancer Society Cancer Facts & Statistics. [Online]. Available: [Accessed: 08-Jul- 2016]. [4] Prostate cancer survival statistics, Cancer Research UK, 15-May [Online]. Available: [Accessed: 08-Jul-2016]. [5] A. Heidenreich, P. J. Bastian, J. Bellmunt, M. Bolla, S. Joniau, T. van der Kwast, M. Mason, V. Matveev, T. Wiegel, F. Zattoni, N. Mottet, and European Association of

8 Urology, EAU guidelines on prostate cancer. part 1: screening, diagnosis, and local treatment with curative intent-update 2013, Eur. Urol., vol. 65, no. 1, pp , Jan [6] E. H. J. Hamoen, M. de Rooij, J. A. Witjes, J. O. Barentsz, and M. M. Rovers, Use of the Prostate Imaging Reporting and Data System (PI-RADS) for Prostate Cancer Detection with Multiparametric Magnetic Resonance Imaging: A Diagnostic Metaanalysis, Eur. Urol., vol. 67, no. 6, pp , Jun [7] M. Umbehr, L. M. Bachmann, U. Held, T. M. Kessler, T. Sulser, D. Weishaupt, J. Kurhanewicz, and J. Steurer, Combined Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy Imaging in the Diagnosis of Prostate Cancer: A Systematic Review and Meta-analysis, Eur. Urol., vol. 55, no. 3, pp , Mar [8] M. Roethke, D. Blondin, H. Schlemmer, and T. Franiel, PI-RADS classification: structured reporting for MRI of the prostate, Rofo, vol. 185, no. 3, pp , [9] J. C. Weinreb, J. O. Barentsz, P. L. Choyke, F. Cornud, M. A. Haider, K. J. Macura, D. Margolis, M. D. Schnall, F. Shtern, C. M. Tempany, H. C. Thoeny, and S. Verma, PI-RADS Prostate Imaging Reporting and Data System: 2015, Version 2, Eur. Urol., vol. 69, no. 1, pp , Jan [10] L. Allainmat, A. Baskin, T. D. Perrot, M. Eiber, M. Souvatzoglou, and J.-P. Vallée, Prostate Cancers, in Atlas of PET/MR Imaging in Oncology, O. Ratib, M. Schwaiger, and T. Beyer, Eds. Springer Berlin Heidelberg, 2013, pp [11] National Alliance for Medical Image Computing. [Online]. Available: [Accessed: 08-Jul-2016]. [12] Insight Journal (ISSN X) - Histogram-based thresholding - some missing methods. [Online]. Available: [Accessed: 08-Jul-2016]. [13] L.-S. Tam, J. F. Griffith, A. B. Yu, T. K. Li, and E. K. Li, Rapid improvement in rheumatoid arthritis patients on combination of methotrexate and infliximab: clinical and magnetic resonance imaging evaluation, Clin. Rheumatol., vol. 26, no. 6, pp , Jul

9 7 Appendix: Interactive user interface Fig. A1. User interface basic design. Top left: coronal view; top right: sagittal view; bottom right: axial view; bottom right: 3D segmentation rendering window. Right: control panels for various processing functions. Fig. A2. Rigid registration with manual initialization and auto align tools. Fig. A3. Prostate segmentation by manual polygon drawing tools.

10 Fig. A5. Prostate and bladder segmentation results. Red: left prostate; blue: right prostate; green: urinal bladder. Fig. A6. Automatic necrosis region labeling by Otsu thresholding. Red: left non-necrosis prostate tissue; blue: right non-necrosis prostate tissue; yellow: left necrosis tissue; cyan: right necrosis tissue; green: urinal bladder.

11 Fig. A7. DCE curve fitting at different labeled regions. Fig. A8. Volumes of the labeled regions.