In this paper, different algorithms for volume reconstruction from tomographic cross-sectional pathology slices are described and tested. A tissue-mimicking phantom made with a mixture of agar and aluminium oxide was sliced at different thickness as per pathological standard guidelines. Phantom model was also virtually sliced and reconstructed in software. Results showed that shape-based spline interpolation method was the most precise, but generated a volume underestimation of 0.5%

E. A. Stach (1466908)

E. A. Sutter (1949533)

K. L. Jungjohann (1949536)

P. W. Sutter (1949539)

S. Bliznakov (1949542)

Using in situ liquid cell electron
microscopy we investigate Pd
growth in dilute aqueous Pd salt solutions containing Au nanoparticle
seeds. Au–Pd core–shell nanostructures are formed via
deposition of Pd0, generated by the reduction of chloropalladate
complexes by radicals, such as hydrated electrons (eaq–) induced by the electron beam in the solution. The
size and shape of the Au seeds determine the morphology of the Pd
shells, via preferential Pd incorporation in low-coordination sites
and avoidance of extended facets. Analysis of the Pd incorporation
on Au particles at different distances from a focused electron beam
provides a quantitative picture of the growth process and shows that
the growth is limited by the diffusion of eaq– in the solution

FigShare

In Situ Liquid Cell Electron Microscopy of the Solution
Growth of Au–Pd Core–Shell Nanostructures

Reines March, Gabriel

Ju, Xiangyang

Marshall, Stephen

University of Strathclyde Institutional Repository

Reines March, Gabriel and Ju, Xiangyang and Marshall, Stephen (2017) Correlation of pre-operative cancer imaging techniques with post-operative gross and microscopic pathology images. In: Irish Machine Vision and Image Processing Conference Proceedings. Irish Pattern Recognition & Classification Society, Ireland, pp. 257-260. ISBN 978-0-9934207-2-6 , This version is available at https://strathprints.strath.ac.uk/62257/Strathprints is  designed  to  allow  users  to  access  the  research  output  of  the  University  of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may  not  engage  in  further  distribution  of  the  material  for  any  profitmaking  activities  or  any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge. Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.Correlation of Pre-Operative Cancer Imaging Techniques withPost-Operative Gross and Microscopic Pathology ImagesGabriel Reines March, Xiangyang Ju, Stephen MarshallMedical Devices Unit, West Glasgow Ambulatory Care Hospital, NHS Greater Glasgow and Clyde,Dalnair St, Glasgow G3 8SJ (UK)Hyperspectral Imaging Centre, Dept. of Electronic and Electrical Engineering, University ofStrathclyde, 204 George St, Glasgow G1 1XW (UK)AbstractIn this paper, different algorithms for volume reconstruction from tomographic cross-sectional pathologyslices are described and tested. A tissue-mimicking phantom made with a mixture of agar and aluminiumoxide was sliced at different thickness as per pathological standard guidelines. Phantom model was alsovirtually sliced and reconstructed in software. Results showed that shape-based spline interpolation methodwas the most precise, but generated a volume underestimation of 0.5%.Keywords: Tomographic Image Processing, Volume Reconstruction, Biomedical Image Processing1 IntroductionMedical imaging techniques have evolved dramatically over the last two decades. The increase in acquisitionand processing speeds, resolution and availability has fostered their widespread use as a diagnostic and assess-ment tool. However, in cancerous tissue, cellular-level features play a fundamental role in determining the stageand type of carcinoma, as well as its local metabolic behaviour. To date, the gold standard for analysing tissueat these high resolutions is by taking a sample (biopsy) and analysing it under a microscope, which is a highlyinvasive procedure. The current study aims to retrospectively bridge the gap between post-operative pathologyand diagnostic imaging, by fusing pathological findings to pre-operative PET-CT scans. Upon alignment ofboth datasets, clinical oncologists will be able to identify and match certain pathological features in the pre-operative images. This new insight could potentially help them to better predict cancer staging and prognosisfrom diagnostic images, therefore avoiding unnecessary surgery.The challenges of this study are the disparity in image resolution between modalities, low sampling ratein the transverse plane and the non-linear deformations undergone by lung tissue during the different clinicalstages. To date, simulations with a synthetic tissue-mimicking phantom have been used to test several volumereconstruction algorithms and methods.2 Materials and MethodsSeveral synthetic tissue-mimicking phantoms have been used to test the performance of the different algorithmsimplemented in this project. Its shape was especially designed in CAD software to include several featuresuseful for testing and challenging different volume reconstruction algorithms (see Figure 1). Materials testedinclude poly(vinyl alcohol) and agar gelatine, the latter showing the most appropriate mechanical properties forour purpose. Aluminium oxide is added as scattering agent to make the material opaque in the x-ray spectrum.Proceedings of the 19th Irish Machine Vision and Image Processing conference IMVIP 2017Aug 30th-Sept 1st, 2017, Maynooth, Ireland 257 ISBN 978-0-9934207-2-6Figure 1: Original phantom designA custom-designed mould has been 3D-printed to produce the phantom samples. Figure 2 shows a samplecross-section of the phantom in both x-ray and visible spectra.One of the main challenges of this project is to truthfully reconstruct the original volume and shape of thetumour from tomographic slices. The main reason behind this complexity is because lung carcinomas can adopthighly unpredictable, heterogeneous shapes, which involve fast-changing features along its surface. Combinedwith the fact that lung tissue has a spongy texture and therefore thin slices cannot be produced without tearingthe sample, reconstructing the specimen volume from discrete pathology slices remains an ill-posed problem,as per basic sampling theorem directives. One of the motivations for using a phantom has been precisely theavailability of a ground truth model to compare our reconstruction against.Shape-based interpolation method has been used, first developed by Raya and Udupa [1]. It consists of firstsegmenting the image to generate a binary mask of the slice, where pixels belonging to the shape of interest arerepresented by boolean true values. Next, the binary image is converted into a grayscale image, wherein thegrey value of a pixel represents the shortest Manhattan distance to the cross-sectional boundary of the binarymask. As a convention, points inside the boundary (i.e. belonging to the shape) are assigned positive distances,and negative values are assigned to outsiders. Those grey values are then interpolated along the z-axis. Thenon-negative values of the resulting volume constitute the interpolated object.In our first experiments, several agar phantoms were embedded in a bespoke slicing rig and slices were madeat regular intervals (2.5 mm). With the use of a digital camera (Canon EOS M3, Canon Inc., Tokyo, Japan),pictures of each cross-section were taken (see example in Figure 2 right). Next, all photographs were processedto segment the phantom region of interest. Two segmentation approaches were tried: colour thresholding andregion growing. The former is based on absolute pixel colour values, whereas the latter depends on the gradientof the scene. In our self-implemented region growing algorithm, the user first needs to interactively select acolour plane, preferably one offering high contrast between the phantom and the surroundings. Three colourspaces are available to choose from, namely RGB, LAB and HSV. Then the user selects a series of seed pixelson the image, which can be any pixel inside the region of interest. If the grey level difference between aProceedings of the 19th Irish Machine Vision and Image Processing conference IMVIP 2017Aug 30th-Sept 1st, 2017, Maynooth, Ireland 258 ISBN 978-0-9934207-2-6Figure 2: Sample phantom cross-section in x-ray (left) and visible spectra (right)pixel and a neighbour is less than a given threshold, then the neighbour is included in the region of interest.Otherwise, the neighbour is defined as an outsider. The algorithm iterates until convergence is reached.3 ResultsInitially, several agar phantoms were physically sliced at 2.5mm intervals. A picture of a fiducial marker ofknown size was also taken to establish a relationship between pixel size and real world units. The phantom areawas segmented using the algorithms mentioned above. The volume recovered on all experiments laid between92% and 93% of the ground truth value (measured to be 48,864mm3).At this point the question was whether this underestimation was intrinsic to the interpolation method, ordue to imprecisions in the segmentation routine, or a combination of both. Therefore, in order to isolate theinterpolation problem, it was proposed to perform a virtual slicing of the original CAD volume and try toreconstruct it from the subset of slices. This way the binary masks were intrinsically defined by the volumecross-sections, avoiding the need to segment the images.When designing the experiment, however, another question arose: what position should we start slicing ourmodel at? The obvious answer seemed to be at the apex. Whilst this is possible with a virtual model, on anembedded phantom or real carcinoma it is very difficult to perform a cut which corresponds to the plane wherethe very first distinguishable feature situated on the apex shows. Therefore, this randomness in the position ofthe first cut had to be accounted for. It was assumed that the first cut could lie in between 0 (i.e. the apex of theobject) and one slice thickness, with a uniform probability.The virtual model was sliced at intervals ranging from 0.5 to 10mm in 0.5mm steps. For each slicethickness and interpolation method (i.e. nearest neighbour, linear and cubic spline), the routine was run 200times, introducing a random offset at each iteration. Results are shown in Figure 3. It can be observed that in theregion of interest (i.e. slices from 1mm up to 5mm thickness), nearest neighbour provides the most accurateresults, and cubic spline shows a systematic volume underestimation of 0.5%. In order to verify these results,the original phantom CADmodel was divided in quarters, and each new shape was reconstructed independentlyusing the same conditions. Results obtained were coherent with those shown in Figure 3.It was also proposed to perform a local comparison between the ground truth model and reconstructedphantom to evaluate which shape features give the most error. For this purpose, both objects are first convertedto point clouds. Then, the reconstructed volume is rigidly registered to the model using Iterative Closest Point(ICP) algorithm. Next, for each point belonging to the generated volume, the distance to its nearest neighbourin the model point cloud is calculated. These distances are then represented in a 3D map, which allows the userto perform a quick qualitative evaluation of the shape reconstructed. After running the routine on the volumesobtained, it was seen that the features giving the largest error were mainly the non-reconstructed shallow valleysand the extrema. These results will be taken into account when designing other phantoms to test our algorithms.Proceedings of the 19th Irish Machine Vision and Image Processing conference IMVIP 2017Aug 30th-Sept 1st, 2017, Maynooth, Ireland 259 ISBN 978-0-9934207-2-6Figure 3: Boxplot showing the volume reconstructed after 200 runs using nearest neighbour (red), linear (blue)and cubic spline (green) interpolation algorithms. Boxes indicate the 25 and 75 percentile of the distribution,horizontal marker indicates the median, and whiskers show the spread of data. Outliers are marked with a crosssymbol. Dotted lines represent the evolution of the mean reconstructed value. Ground truth (48,864mm3) isindicated with a dash-dotted black line.4 Future workFuture work will be focused on three main areas. Morphology-based interpolation: the method proposedin [2, 3] is currently being implemented. It is then going to be tested using the same dataset as used in theshape-based interpolation experiment, so that results can be compared. This method is expected to show betterperformance around the extrema regions and better handling of topological changes. Comparison of our algo-rithms vs commercial software: the phantom will be scanned in a Computerised Tomography (CT) scannerand its boundary will be delineated by an oncologist on a commercial radiotherapy planning unit. Resultingvolume will be compared against ground truth and our implementation. Trial on human tissue: volume re-construction algorithms will be tested on actual lung tissue. Hyperspectral imaging will be tested to explorewhether cancerous tissue presents a different absorption spectrum in the near IR region compared to healthytissue. Automatic tumour segmentation approaches will also be investigated in both gross and pathology spec-imens.References[1] S. P. Raya and J. K. Udupa, Shape-Based Interpolation of Multidimensional Objects, IEEE T Med Imaging9(1), 32-42 (1990)[2] A. B. Albu, T. Beugeling and D. Laurendau, A Morphology-Based Approach for Interslice Interpolation ofAnatomical Slices From Volumetric Images, IEEE T Bio-Med Eng, 55(8), 2022-2038 (2008)[3] J. Vidal, J. Crespo, V. Maojo, “Recursive Interpolation Technique for Binary Images Based on Morpholog-ical Median Sets”, in Mathematical Morphology: 40 Years On, C. Ronse et al., eds. (Springer, 2005)Proceedings of the 19th Irish Machine Vision and Image Processing conference IMVIP 2017Aug 30th-Sept 1st, 2017, Maynooth, Ireland 260 ISBN 978-0-9934207-2-6