Shape localization, quantification and correspondence using Region Matching Algorithm

We propose a method for local, region-based matching of planar shapes, especially as those shapes that change over time. This is a problem fundamental to medical imaging, specifically the comparison over time of mammograms. The method is based on the non-emergence and non-enhancement of maxima, as well as the causality principle of integral invariant scale space. The core idea of our Region Matching Algorithm (RMA) is to divide a shape into a number of “salient” regions and then to compare all such regions for local similarity in order to quantitatively identify new growths or partial/complete occlusions. The algorithm has several advantages over commonly used methods for shape comparison of segmented regions. First, it provides improved key-point alignment for optimal shape correspondence. Second, it identifies localized changes such as new growths as well as complete/partial occlusion in corresponding regions by dividing the segmented region into sub-regions based upon the extrema that persist over a sufficient range of scales. Third, the algorithm does not depend upon the spatial locations of mammographic features and eliminates the need for registration to identify salient changes over time. Finally, the algorithm is fast to compute and requires no human intervention. We apply the method to temporal pairs of mammograms in order to detect potentially important differences between them

Integral invariants for image enhancement

Medical images pose a major challenge for image analysis: often they have poor signal-to-noise, necessitating smoothing; yet such smoothing needs to preserve the boundaries of regions of interest and small features such as mammogram microcalcifications. We show how circular integral invariants (II) may be adapted for feature-preserving smoothing to facilitate segmentation. Though II is isotropic, we show that it leads to considerably less feature deterioration than Gaussian blurring and it improves segmentation of regions of interest as compared to anisotropic diffusion, particularly for hierarchical contour based segmentation methods

Personal Tutorage System for Schools in Pakistan: A Policy Proposal

The core objective of a school is to develop children for challenges ahead in life ahead by passing them through a well-developed mechanise process of teaching, learning and scholarship. While teaching helps them to grow academically, learning and scholarship are essential for personal development. In a participatory, progressive and tolerant society, the role of a teacher is to help children in becoming positively ‘participating citizens’ (Sociology and 1967, 1967). In Pakistan, there is a huge emphasis on exam-oriented quantitative teaching practices within government schools; however, the learning and scholarship aspect seems to have been largely left neglected. Currently, the only opportunity for a child to have a mentor-oriented learning experience within the school is through subject classes. Unfortunately, these classes do provide subject-specific guidance but does address after the overall development needs of a child, in particular: personal development, their trajectory in achieving their full potential, hitting learning objectives through the schooling experience and in developing ways of self-reflection (Miles, 1985; Syed et al., 2007a, 2007b). The paper is focused on proposing the Personal Tutorage system and structure in Pakistan, nevertheless, it can be implemented with minor revisions anywhere else in the world. The purpose of the draft is to propose a system of Personal Tutorage to be introduced at the government schools in Pakistan. For the benefit of the intended political and bureaucratic audience, the authors have intentionally kept the discussion closely relevant to the very structure of the proposed system and have avoided unnecessary jargon, as well as a detailed literature review. The draft is by no means suggested as a final proposal and has a substantial room for updates and structural improvements. Please note that elements or resources within to furnish this proposal may not be at present available in schools. Here, the word Personal Tutor refers to a member of staff in a school, typically a teacher; tutees refer to the students

False positive reduction in CADe using diffusing scale space

Segmentation is typically the first step in computer-aided-detection (CADe). The second step is false positive reduction which usually involves computing a large number of features with thresholds set by training over excessive data set. The number of false positives can, in principle, be reduced by extensive noise removal and other forms of image enhancement prior to segmentation. However, this can drastically affect the true positive results and their boundaries. We present a post-segmentation method to reduce the number of false positives by using a diffusion scale space. The method is illustrated using Integral Invariant scale space, though this is not a requirement. It is quite general, does not require any prior information, is fast and easy to compute, and gives very encouraging results. Experiments are performed both on intensity mammograms as well as on Volpara® density maps

Tracking ‘developing’ Focal Densities in Breast Quadrants

Abstract Background Focal density (FD) is a dense mammographic region that cannot be accurately identified as a mass without further examination. If a particular breast quadrant is significantly dense than others or has an increase in density over time, this could be associated with neoplasm especially in the presence of a tangible mass. We have developed a method to study and track quadrant-wise increase in FD over time. Method A set of 10 temporal patient cases collected over a period of up to 6 years were used. Each quarter of the breast is assigned a FD score, where quadrants are defined by first differentiating a border between the breast region and skin line. Then a nipple detection method is used to correctly identify nipples, including those ‘not in profile’. Afterward, the nipple location is used as a reference point to divide the breast into four quarters (see Figure 2). Further on, FDs are quantified [1] and a score assigned to each quadrant of the breast, and to the breast as a whole. Results Results show that our method can detect increase in FD over time in some quarters of breast; a finding that can be verified by Volpara density grade [2]. It can be seen (Figure-2) that Q1-left (upper-interior-UI) has a significantly higher FD score as compared others. Clinical evaluation for this BIRADS-C mammogram (Figure-1, left craniocaudal) confirms the presence of 6mm grade-4 screen detected invasive lobular carcinoma in the left UI quadrant of the breast. Figure-3 shows a FD comparison of all quadrats of the bilateral pair over the course of 6 years. Q1-left remained the densest throughout. Conclusion The study suggests that tracking FD (both ‘developing’ and ‘stable’) over time could potentially help in better understanding of the risk of breast cancer development in any particular quadrant of the breast

Shape matching by integral invariants on eccentricity transformed images

Matching occluded and noisy shapes is a frequently encountered problem in vision and medical image analysis and more generally in computer vision. To keep track of changes inside breast, it is important for a computer aided diagnosis system (CAD) to establish correspondences between regions of interest. Shape transformations, computed both with integral invariants and geodesic distance yield signatures that are invariant to isometric deformations, such as bending and articulations. Integral invariants are used on 2D planar shapes to describe the shape boundary. However, they provide no information about where a particular feature on the boundary lies with regard to overall shape structure. On the other hand, eccentricity transforms can be used to match shapes by signatures of geodesic distance histograms based on information from inside the shape; but they ignore the boundary information. We describe a method that combines both the boundary signature of shape obtained from integral invariants and structural information from the eccentricity transform to yield improved results

Learning spatiotemporal features for esophageal abnormality detection from endoscopic videos

Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and early can- cerous) can improve the survival rate of the patients. Re- cent deep learning-based methods for selected types of esophageal abnormality detection from endoscopic images have been proposed. However, no methods have been introduced in the literature to cover the detection from endoscopic videos, detection from challenging frames and detection of more than one esophageal abnormality type. In this paper, we present an efficient method to automat- ically detect different types of esophageal abnormalities from endoscopic videos. We propose a novel 3D Sequen- tial DenseConvLstm network that extracts spatiotemporal features from the input video. Our network incorporates 3D Convolutional Neural Network (3DCNN) and Convolu- tional Lstm (ConvLstm) to efficiently learn short and long term spatiotemporal features. The generated feature map is utilized by a region proposal network and ROI pooling layer to produce a bounding box that detects abnormal- ity regions in each frame throughout the video. Finally, we investigate a post-processing method named Frame Search Conditional Random Field (FS-CRF) that improves the overall performance of the model by recovering the missing regions in neighborhood frames within the same clip. We extensively validate our model on an endoscopic video dataset that includes a variety of esophageal ab- normalities. Our model achieved high performance using different evaluation metrics showing 93.7% recall, 92.7% precision, and 93.2% F-measure. Moreover, as no results have been reported in the literature for the esophageal abnormality detection from endoscopic videos, to validate the robustness of our model, we have tested the model on a publicly available colonoscopy video dataset, achieving the polyp detection performance in a recall of 81.18%, precision of 96.45% and F-measure 88.16%, compared to the state-of-the-art results of 78.84% recall, 90.51% preci- sion and 84.27% F-measure using the same dataset. This demonstrates that the proposed method can be adapted to different gastrointestinal endoscopic video applications with a promising performance

ResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images

Echocardiography is the modality of choice for the assessment of left ventricle function. Left ventricle is responsible for pumping blood rich in oxygen to all body parts. Segmentation of this chamber from echocardiographic images is a challenging task, due to the ambiguous boundary and inhomogeneous intensity distribution. In this paper we propose a novel deep learning model named ResDUnet. The model is based on U-net incorporated with dilated convolution, where residual blocks are employed instead of the basic U-net units to ease the training process. Each block is enriched with squeeze and excitation unit for channel-wise attention and adaptive feature re-calibration. To tackle the problem of left ventricle shape and size variability, we chose to enrich the process of feature concatenation in U-net by integrating feature maps generated by cascaded dilation. Cascaded dilation broadens the receptive field size in comparison with traditional convolution, which allows the generation of multi-scale information which in turn results in a more robust segmentation. Performance measures were evaluated on a publicly available dataset of 500 patients with large variability in terms of quality and patients pathology. The proposed model shows a dice similarity increase of 8.4% when compared to deeplabv3 and 1.2% when compared to the basic U-net architecture. Experimental results demonstrate the potential use in clinical domain

Breast3D: An Augmented Reality System for Breast CT and MRI

Adoption of Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) - known collectively as Extended Reality (XR) devices has been rapidly increasing over recent years. However, the focus of XR research has shown a lack of diversity in solutions to the problems within medicine, with it being predominantly focused in augmenting surgical procedures. Whilst important, XR applied to aiding medical diagnosis and surgical planning is relatively unexplored. In this paper we present a fully functional mammographic image analysis system, Breast3D, that can reconstruct MRI and CT scan data in XR. With breast cancer Breast Imaging-Reporting and Data System (BI-RADS) risk lexicon, early detection and clinical workflow such as Multi-disciplinary team (MDT) meetings for cancer in mind, our new mammography visualization system reconstructs CT and MRI volumes in a real 3D space. Breast3D is built upon the past literature and inspired from research for diagnosis and surgical planning. In addition to visualising the models in MR using the Microsoft HoloLens, Breast3D is versatile and portable to different XR head-mounted displays such as HTC Vive. Breast3D demonstrates the early potential for XR within diagnostics of 3D mammographic modalities, an application that has been proposed but until now has not been implemented

ResDUnet: A Deep Learning based Left Ventricle Segmentation Method for Echocardiography

Segmentation of echocardiographic images is an essential step for assessing the cardiac functionality and providing indicative clinical measures, and all further heart analysis relies on the accuracy of this process. However, the fuzzy nature of echocardiographic images degraded by distortion and speckle noise poses some challenges on the manual segmentation task. In this paper, we propose a fully automated left ventricle segmentation method that can overcome those challenges. Our method performs accurate delineation for the ventricle boundaries despite the ill-defined borders and shape variability of the left ventricle. The well-known deep learning segmentation model, known as the U-net, has addressed some of these challenges with outstanding performance. However, it still ignores the contribution of all semantic information through the segmentation process. Here we propose a novel deep learning segmentation method based on U-net, named ResDUnet. It incorporates feature extraction at different scales through the integration of cascaded dilated convolution. To ease the training process, residual blocks are deployed instead of the basic U-net blocks. Each residual block is enriched with a squeeze and excitation unit for channel-wise attention and adaptive feature re-calibration. The performance of the method is evaluated on a dataset of 2000 images acquired from 500 patients with large variability in quality and patient pathology. ResDUnet outperforms state-of-the-art methods with a Dice similarity increase of 8.4% and 1.2% compared to deeplabv3 and U-net, respectively. Furthermore, to demonstrate the impact of each proposed sub-module, several experiments have been carried out with different designs and variations of the integrated sub-modules. We also describe and discuss all technical elements of a deep-learning model via a step-by-step explanation of parameters and methods, while using our left ventricle segmentation as a case study, to explain the application of AI to echocardiographic imaging