ÐThis paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, which is a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized by explicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob, which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses and the original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness of the proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratio in the resultant volume-rendered images with variable opacity values.

Aims: Evaluating expression of the human epidermal growth factor receptor 2 (HER2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognized importance as a predictive and prognostic marker in clinical practice. However, visual scoring of HER2 is subjective, and consequently prone to interobserver variability. Given the prognostic and therapeutic implications of HER2 scoring, a more objective method is required. In this paper, we report on a recent automated HER2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art artificial intelligence (AI)-based automated methods for HER2 scoring. Methods and results: The contest data set comprised digitized whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both haematoxylin and eosin (H&E) and IHC for HER2. The contesting algorithms predicted scores of the IHC slides automatically for an unseen subset of the data set and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple 'Man versus Machine' contest for the scoring of HER2 and show Address for correspondence: N Rajpoot and T Qaiser, Department of Computer Science, University of Warwick, UK. e-mails: n.m.rajpoot@ warwick.ac.uk; t.qaiser@warwick.ac.uk *These authors contributed equally to this study. 2018 , 72, 227-238. DOI: 10.1111 that the automated methods could beat the pathology experts on this contest data set. Conclusions: This paper presents a benchmark for comparing the performance of automated algorithms for scoring of HER2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.

A feature selection methodology based on a novel Bhattacharyya Space is presented and illustrated with a texture segmentation problem. The Bhattacharyya Space is constructed from the Bhattacharyya distances of different measurements extracted with sub-band filters from training samples. The marginal distributions of the Bhattacharyya Space present a sequence of the most discriminant sub-bands that can be used as a path for a wrapper algorithm. When this feature selection is used with a multiresolution classification algorithm on a standard set of texture mosaics, it produces the lowest misclassification errors reported.

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This paper presents a vascular representation and segmentation algorithm based on a multiresolution Hermite model (MHM). A two-dimensional Hermite function intensity model is developed which models blood vessel profiles in a quad-tree structure over a range of spatial resolutions. The use of a multiresolution representation simplifies the image modeling and allows for a robust analysis by combining information across scales. Estimation over scale also reduces the overall computational complexity. As well as using MHM for vessel labelling, the local image modeling can accurately represent vessel directions, widths, amplitudes, and branch points which readily enable the global topology to be inferred. An expectation-maximization (EM) type of optimization scheme is used to estimate local model parameters and an information theoretic test is then applied to select the most appropriate scale/feature model for each region of the image. In the final stage, Bayesian stochastic inference is employed for linking the local features to obtain a description of the global vascular structure. After a detailed description and analysis of MHM, experimental results on two standard retinal databases are given that demonstrate its comparative performance. These show MHM to perform comparably with other retinal vessel labelling methods.

OBJECTIVE: We used three-dimensional reconstructed magnetic resonance images for planning the operations of 16 patients with various cerebrovascular diseases. We studied the cases of these patients to determine the advantages and current limitations of our computer-assisted surgical planning system as it applies to the treatment of vascular lesions.METHODS: Magnetic resonance angiograms or thin slice gradient echo magnetic resonance images were processed for three-dimensional reconstruction. The segmentation, based on the signal intensities and voxel connectivity, separated each anatomic structure of interest, such as the brain, vessels, and skin. A threedimensional model was then reconstructed by surface rendering. This three-dimensional model could be colored, made translucent, and interactively rotated by a mouse-controlled cursor on a workstation display. In addition, a three-dimensional blood flow analysis was performed, if necessary. The three-dimensional model was used to assist in three stages of surgical planning, as follows: 1) to choose the best method of intervention, 2) to evaluate surgical risk, 3) to select a surgical approach, and 4) to localize lesions.RESULTS: The generation of three-dimensional models allows visualization of pathological anatomy and its relationship to adjacent normal structures, accurate lesion volume determination, and preoperative computerassisted visualization of alternative surgical approaches.CONCLUSION: Computer-assisted surgical planning is useful for patients with cerebrovascular disease at various stages of treatment. Lesion identification, therapeutic and surgical option planning, and intraoperative localization are all enhanced with these techniques.Current advances in computer and imaging technologies have made it possible to generate precise threedimensional reconstructed images in several hours using routine clinical radiological data (2,6,17,19,25). We have created a complete computer-assisted surgical planning system to provide three-dimensional models for surgery (14,17) and have already applied it in more than 120 cases.For cerebrovascular disease, conventional or digital subtraction angiography is used to plan treatment. These modalities generate a high-resolution image, but it is sometimes difficult to understand the precise anatomy of the lesion and related structures because they are only two-dimensionally projected images. On the other hand, the complementary and less invasive modalities, such as computed tomography, magnetic resonance(MR) imaging, and MR angiography, contain three-dimensional volume data. More recently, three-dimensional computed tomographic (CT) angiography has been developed and applied for diagnostic and therapeutic purposes (2,6,16,22,25).These three-dimensional reconstructed images are expected to help a surgeon select the most appropriate therapeutic intervention. This is achieved through "preoperative simulation" of the surgical approach and real-time intraoperative navigational methods. In addition, precise three-dimens...

Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. In this paper a Multiresolution Volumetric Texture Segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via subband filtering using an Orientation Pyramid [1]. A novel Bhattacharyya space, based on the Bhattacharyya distance, is proposed for selecting the most discriminant measurements and producing a compact feature space. An oct tree is built of the multivariate features space and a chosen level at a lower spatial resolution is first classified. The classified voxel labels are then projected to lower levels of the tree where a boundary refinement procedure is performed with a 3D equivalent of butterfly filters. The algorithm was tested in 3D with artificial data and three Magnetic Resonance Imaging sets of human knees with encouraging results. The regions segmented from the knees correspond to anatomical structures that can be used as a starting point for other measurements such as cartilage extraction.

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