Segmentation of vessel structures from photoacoustic images with reliability assessment.

Photoacoustic imaging enables the imaging of soft biological tissue with combined optical contrast and ultrasound resolution. One of the targets of interest is tissue vasculature. However, the photoacoustic images may not directly provide the information on, for example, vasculature structure. Therefore, the images are improved by reducing noise and artefacts, and processed for better visualisation of the target of interest. In this work, we present a new segmentation method of photoacoustic images that also straightforwardly produces assessments of its reliability. The segmentation depends on parameters which have a natural tendency to increase the reliability as the parameter values monotonically change. The reliability is assessed by counting classifications of image voxels with different parameter values. The resulting segmentation with reliability offers new ways and tools to analyse photoacoustic images and new possibilities for utilising them as anatomical priors in further computations. Our MATLAB implementation of the method is available as an open-source software package [P. Raumonen, Matlab, 2018].

[1]  Lihong V. Wang,et al.  Automatic algorithm for skin profile detection in photoacoustic microscopy. , 2009, Journal of biomedical optics.

[2]  Jan Laufer,et al.  Photoacoustic imaging using genetically encoded reporters: a review , 2017, Journal of biomedical optics.

[3]  Tanja Tarvainen,et al.  Image reconstruction with uncertainty quantification in photoacoustic tomography. , 2016, The Journal of the Acoustical Society of America.

[4]  Lihong V. Wang,et al.  Reconstructions in limited-view thermoacoustic tomography. , 2004, Medical physics.

[5]  S. Arridge,et al.  Quantitative spectroscopic photoacoustic imaging: a review. , 2012, Journal of biomedical optics.

[6]  Vasilis Ntziachristos,et al.  Accurate Model-Based Reconstruction Algorithm for Three-Dimensional Optoacoustic Tomography , 2012, IEEE Transactions on Medical Imaging.

[7]  S Y Emelianov,et al.  Skeletonization algorithm-based blood vessel quantification using in vivo 3D photoacoustic imaging , 2016, Physics in medicine and biology.

[8]  B T Cox,et al.  k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields. , 2010, Journal of biomedical optics.

[9]  Lihong V. Wang,et al.  Universal back-projection algorithm for photoacoustic computed tomography. , 2005 .

[10]  Ashkan Javaherian,et al.  A Multi-Grid Iterative Method for Photoacoustic Tomography , 2016, IEEE Transactions on Medical Imaging.

[11]  Lihong V. Wang,et al.  Small-Animal Whole-Body Photoacoustic Tomography: A Review , 2014, IEEE Transactions on Biomedical Engineering.

[12]  Lihong V. Wang,et al.  A practical guide to photoacoustic tomography in the life sciences , 2016, Nature Methods.

[13]  M. Haltmeier,et al.  Exact and approximative imaging methods for photoacoustic tomography using an arbitrary detection surface. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  M. Anastasio,et al.  Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography , 2012, Physics in medicine and biology.

[15]  Lihong V. Wang,et al.  Time reversal and its application to tomography with diffracting sources. , 2004, Physical review letters.

[16]  Manojit Pramanik,et al.  Basis pursuit deconvolution for improving model-based reconstructed images in photoacoustic tomography. , 2014, Biomedical optics express.

[17]  Bradley E. Treeby,et al.  Artifact Trapping During Time Reversal Photoacoustic Imaging for Acoustically Heterogeneous Media , 2010, IEEE Transactions on Medical Imaging.

[18]  Marta Betcke,et al.  Accelerated high-resolution photoacoustic tomography via compressed sensing , 2016, Physics in medicine and biology.

[19]  Jan Laufer,et al.  Backward-mode multiwavelength photoacoustic scanner using a planar Fabry-Perot polymer film ultrasound sensor for high-resolution three-dimensional imaging of biological tissues. , 2008, Applied optics.

[20]  Weimin Zhou,et al.  Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging , 2017, Journal of biomedical optics.

[21]  P. Beard Biomedical photoacoustic imaging , 2011, Interface Focus.

[22]  Lihong V Wang,et al.  Photoacoustic tomography and sensing in biomedicine , 2009, Physics in medicine and biology.

[23]  Mark A. Anastasio,et al.  A Forward-Adjoint Operator Pair Based on the Elastic Wave Equation for Use in Transcranial Photoacoustic Computed Tomography , 2017, SIAM J. Imaging Sci..

[24]  Sarah E Bohndiek,et al.  Contrast agents for molecular photoacoustic imaging , 2016, Nature Methods.

[25]  Simon R. Arridge,et al.  Bayesian Image Reconstruction in Quantitative Photoacoustic Tomography , 2013, IEEE Transactions on Medical Imaging.

[26]  Daniel Razansky,et al.  Visual Quality Enhancement in Optoacoustic Tomography Using Active Contour Segmentation Priors , 2015, IEEE Transactions on Medical Imaging.