Automatic correspondence between 2D and 3D images of the breast

Radiologists often need to localise corresponding findings in different images of the breast, such as Magnetic Resonance Images and X-ray mammograms. However, this is a difficult task, as one is a volume and the other a projection image. In addition, the appearance of breast tissue structure can vary significantly between them. Some breast regions are often obscured in an X-ray, due to its projective nature and the superimposition of normal glandular tissue. Automatically determining correspondences between the two modalities could assist radiologists in the detection, diagnosis and surgical planning of breast cancer. This thesis addresses the problems associated with the automatic alignment of 3D and 2D breast images and presents a generic framework for registration that uses the structures within the breast for alignment, rather than surrogates based on the breast outline or nipple position. The proposed algorithm can adapt to incorporate different types of transformation models, in order to capture the breast deformation between modalities. The framework was validated on clinical MRI and X-ray mammography cases using both simple geometrical models, such as the affine, and also more complex ones that are based on biomechanical simulations. The results showed that the proposed framework with the affine transformation model can provide clinically useful accuracy (13.1mm when tested on 113 registration tasks). The biomechanical transformation models provided further improvement when applied on a smaller dataset. Our technique was also tested on determining corresponding findings in multiple X-ray images (i.e. temporal or CC to MLO) for a given subject using the 3D information provided by the MRI. Quantitative results showed that this approach outperforms 2D transformation models that are typically used for this task. The results indicate that this pipeline has the potential to provide a clinically useful tool for radiologists

Social pedagogy and social work relations in Greece: autonomous trajectories

This article explores the relationship between social pedagogy and social work in Greece. The search begins with the identification of their philosophical roots, which, although they are common and start from the Ancient Greek philosophers, have led the course of each discipline in a different direction. What follows is the presentation of the most important defining elements of the development of the studies of social work and social pedagogy in Greece, which include features and historical landmarks. The different trajectories can be seen from the development of studies, where social work has a long tradition as an academic discipline, whereas the academic tradition of social pedagogy is much shorter. A similar differentiation is found in the professional frameworks of social work and social pedagogy in Greece, that is, in the institutionalisation of the profession of social worker and social pedagogue. Indicative data from the field of research of each discipline are then presented. Despite the differences and the autonomous trajectories, remarkable commonalities and similarities between social pedagogy and social work in Greece are identified, such as some basic principles, priorities, epistemological and methodological dimensions and some common areas of interest and action. Therefore, the autonomous trajectories of these disciplines do not separate them, but as potentially complementary, are able to make interdisciplinary connections between them, so that prevention and intervention programmes, especially in the fields of education and the community, can be developed

LEARNING TO DOWNSAMPLE FOR SEGMENTATION OF ULTRA-HIGH RESOLUTION IMAGES

Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work, we demonstrate that this assumption can harm the segmentation performance because the segmentation difficulty varies spatially (see Figure 1 “Uniform”). We combat this problem by introducing a learnable downsampling module, which can be optimised together with the given segmentation model in an end-to-end fashion. We formulate the problem of training such downsampling module as optimisation of sampling density distributions over the input images given their low-resolution views. To defend against degenerate solutions (e.g. over-sampling trivial regions like the backgrounds), we propose a regularisation term that encourages the sampling locations to concentrate around the object boundaries. We find the downsampling module learns to sample more densely at difficult locations, thereby improving the segmentation performance (see Figure 1 "Ours"). Our experiments on benchmarks of high-resolution street view, aerial and medical images demonstrate substantial improvements in terms of efficiency-and-accuracy trade-off compared to both uniform downsampling and two recent advanced downsampling techniques

Combined Reconstruction and Registration of Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) has the potential to en- hance breast cancer detection by reducing the confounding e ect of su- perimposed tissue associated with conventional mammography. In addi- tion the increased volumetric information should enable temporal datasets to be more accurately compared, a task that radiologists routinely apply to conventional mammograms to detect the changes associated with ma- lignancy. In this paper we address the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their reg- istration. Using a simple test object, and DBT simulations from in vivo breast compressions imaged using MRI, we demonstrate that this com- bined reconstruction and registration approach produces improvements in both the reconstructed volumes and the estimated transformation pa- rameters when compared to performing the tasks sequentially

Foveation for Segmentation of Mega-Pixel Histology Images

Segmenting histology images is challenging because of the sheer size of the images with millions or even billions of pixels. Typical solutions pre-process each histology image by dividing it into patches of fixed size and/or down-sampling to meet memory constraints. Such operations incur information loss in the field-of-view (FoV) (i.e., spatial coverage) and the image resolution. The impact on segmentation performance is, however, as yet understudied. In this work, we first show under typical memory constraints (e.g., 10G GPU memory) that the trade-off between FoV and resolution considerably affects segmentation performance on histology images, and its influence also varies spatially according to local patterns in different areas (see Fig. 1). Based on this insight, we then introduce foveation module, a learnable “dataloader” which, for a given histology image, adaptively chooses the appropriate configuration (FoV/resolution trade-off) of the input patch to feed to the downstream segmentation model at each spatial location (Fig. 1). The foveation module is jointly trained with the segmentation network to maximise the task performance. We demonstrate, on the Gleason2019 challenge dataset for histopathology segmentation, that the foveation module improves segmentation performance over the cases trained with patches of fixed FoV/resolution trade-off. Moreover, our model achieves better segmentation accuracy for the two most clinically important and ambiguous classes (Gleason Grade 3 and 4) than the top performers in the challenge by 13.1% and 7.5%, and improves on the average performance of 6 human experts by 6.5% and 7.5%

A Simulation Study of Spectral Cerenkov Luminescence Imaging for Tumour Margin Estimation

Breast cancer is the most common cancer in women in the world. Breast-conserving surgery (BCS) is a standard surgical treatment for breast cancer with the key objective of removing breast tissue, maintaining a negative surgical margin and providing a good cosmetic outcome. A positive surgical margin, meaning the presence of cancerous tissues on the surface of the breast specimen after surgery, is associated with local recurrence after therapy. In this study, we investigate a new imaging modality based on Cerenkov luminescence imaging (CLI) for the purpose of detecting positive surgical margins during BCS. We develop Monte Carlo (MC) simulations using the Geant4 nuclear physics simulation toolbox to study the spectrum of photons emitted given 18F-FDG and breast tissue properties. The resulting simulation spectra show that the CLI signal contains information that may be used to estimate whether the cancerous cells are at a depth of less than 1 mm or greater than 1 mm given appropriate imaging system design and sensitivity. The simulation spectra also show that when the source is located within 1 mm of the surface, the tissue parameters are not relevant to the model as the spectra do not vary significantly. At larger depths, however, the spectral information varies significantly with breast optical parameters, having implications for further studies and system design. While promising, further studies are needed to quantify the CLI response to more accurately incorporate tissue specific parameters and patient specific anatomical details

Automated Classification of Breast Cancer Stroma Maturity from Histological Images

OBJECTIVE: The tumour microenvironment plays a crucial role in regulating tumour progression by a number of different mechanisms, in particular the remodelling of collagen fibres in tumour-associated stroma, which has been reported to be related to patient survival. The underlying motivation of this work is that remodelling of collagen fibres gives rise to observable patterns in Hematoxylin and Eosin (H&E) stained slides from clinical cases of invasive breast carcinoma that the pathologist can label as mature or immature stroma. The aim of this paper is to categorise and automatically classify stromal regions according to their maturity and show that this classification agrees with that of skilled observers, hence providing a repeatable and quantitative measure for prognostic studies. METHODS: We use multi-scale Basic Image Features (BIF) and Local Binary Patterns (LBP), in combination with a random decision trees classifier for classification of breast cancer stroma regions-ofinterest (ROI). RESULTS: We present results from a cohort of 55 patients with analysis of 169 ROI. Our multi-scale approach achieved a classification accuracy of 84%. CONCLUSION: This work demonstrates the ability of texture-based image analysis to differentiate breast cancer stroma maturity in clinically acquired H&E stained slides at least as well as skilled observers

Symmetric Biomechanically Guided Prone-to-Supine Breast Image Registration

Prone-to-supine breast image registration has potential application in the fields of surgical and radiotherapy planning, image guided interventions, and multi-modal cancer diagnosis, staging, and therapy response prediction. However, breast image registration of three dimensional images acquired in different patient positions is a challenging problem, due to large deformations induced to the soft breast tissue caused by the change in gravity loading. We present a symmetric, biomechanical simulation based registration framework which aligns the images in a central, virtually unloaded configuration. The breast tissue is modelled as a neo-Hookean material and gravity is considered as the main source of deformation in the original images. In addition to gravity, our framework successively applies image derived forces directly into the unloading simulation in place of a subsequent image registration step. This results in a biomechanically constrained deformation. Using a finite difference scheme avoids an explicit meshing step and enables simulations to be performed directly in the image space. The explicit time integration scheme allows the motion at the interface between chest and breast to be constrained along the chest wall. The feasibility and accuracy of the approach presented here was assessed by measuring the target registration error (TRE) using a numerical phantom with known ground truth deformations, nine clinical prone MRI and supine CT image pairs, one clinical prone-supine CT image pair and four prone-supine MRI image pairs. The registration reduced the mean TRE for the numerical phantom experiment from initially 19.3 to 0.9 mm and the combined mean TRE for all fourteen clinical data sets from 69.7 to 5.6 mm

Tomosynthesis method for depth resolution of beta emitters

The motivation of this study derives from the need for tumour margin estimation after surgical excision. Conventional beta autoradiography of beta emitters can be used to image tissue sections providing high spatial resolution compared to in-vivo molecular imaging. However, it requires sectioning of the specimen and it provides a 2D image of the tissue. Imaging of the 3D tissue sample can be achieved either by imaging sequential 2D sections, which is time-consuming and laborious, or by using a specialised detector for imaging that records the particles’ direction, in addition to their position, when they hit the detector. In this work we investigate whether a novel beta-tomosynthesis approach can be used for depth resolution of beta emitters. The technique involves acquiring multiple 2D images of the intact tissue sample while the detector rotates around the sample. The images are then combined and used to reconstruct the 3D position of the sources from a limited angle of conventional 2D autoradiography images. We present the results from Geant4 forward simulations and the reconstructed images from a breast tissue sample containing a Fluorine-18 positron emission source. The experiments show that the proposed method can provide depth resolution under certain conditions, indicating that there is potential for its use as a 3D molecular imaging technique of surgical samples in the future

Flexible scintillator autoradiography for tumor margin inspection using 18F-FDG

Autoradiography potentially offers high molecular sensitivity and spatial resolution for tumor margin estimation. However, conventional autoradiography requires sectioning the sample which is destructive and labor-intensive. Here we describe a novel autoradiography technique that uses a flexible ultra-thin scintillator which conforms to the sample surface. Imaging with the flexible scintillator enables direct, high-resolution and high-sensitivity imaging of beta particle emissions from targeted radiotracers. The technique has the potential to identify positive tumor margins in fresh unsectioned samples during surgery, eliminating the processing time demands of conventional autoradiography. We demonstrate the feasibility of the flexible autoradiography approach to directly image the beta emissions from radiopharmaceuticals using lab experiments and GEANT-4 simulations to determine i) the specificity for 18 F compared to 99m Tc-labeled tracers ii) the sensitivity to detect signal from various depths within the tissue. We found that an image resolution of 1.5 mm was achievable with a scattering background and we estimate a minimum detectable activity concentration of 0.9 kBq/ml for 18 F. We show that the flexible autoradiography approach has high potential as a technique for molecular imaging of tumor margins using 18 F-FDG in a tumor xenograft mouse model imaged with a radiation-shielded EMCCD camera. Due to the advantage of conforming to the specimen, the flexible scintillator showed significantly better image quality in terms of tumor signal to whole-body background noise compared to rigid and optimally thick CaF 2 :Eu and BC400. The sensitivity of the technique means it is suitable for clinical translation