Consistent and elastic registration of histological sections using vector-spline regularization

The final publication is available at Springer via http://dx.doi.org/10.1007/11889762_8Revised Papers on Second International ECCV Workshop, CVAMIA 2006 Graz, Austria, May 12, 2006Here we present a new image registration algorithm for the alignment of histological sections that combines the ideas of B-spline based elastic registration and consistent image registration, to allow simultaneous registration of images in two directions (direct and inverse). In principle, deformations based on B-splines are not invertible. The consistency term overcomes this limitation and allows registration of two images in a completely symmetric way. This extension of the elastic registration method simplifies the search for the optimum deformation and allows registering with no information about landmarks or deformation regularization. This approach can also be used as the first step to solve the problem of group-wise registration.Ignacio Arganda-Carreras is being supported by a predoctoral FPI-CAM fellow- ship since October 2003. Carlos Ortiz-de-Solorzano is supported by a Ramon y Cajal (Spanish Ministry of Education and Science ryc-2004-002353) and a Marie Curie International Reintegration Grant (FP6-518688). Jan Kybic was sponsored by the Czech Ministery of Education under project number MSM210000012. Par- tial support is acknowledged to Comunidad de Madrid through grant GR/SAL/0234, to Instituto de Salud Carlos III-Fondo de Investigaciones Sanitarias (FIS) through the IM3 Network and grant 040683 and to the Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica (I+D+I)

Registration and 3D reconstruction of histological sections: application to mammary gland development

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, mayo de 200

Deformable Image Registration Using Convolutional Neural Networks for Connectomics

Department of Computer Science and EngineeringIn this thesis, a new novel method to align two images with recent deep learning scheme called ssEMnet is presented. The reconstruction of serial-section electron microscopy (ssEM) images gives critical insight to neuroscientist understanding real brains. However, alignment of each ssEM plane is not straightforward because of its densely twisted circuit structures. In addition, dynamic deformations are applied to images in the process of acquiring ssEM dataset from specimens. Even worse, non-matched artifacts like dusts and folds occur in the EM images. In recent deep learning researches, especially related with convolutional neural networks (CNNs) have shown to be able to handle various problems in computer vision area. However, there is no clear success on ssEM image registration problem using CNNs. ssEMnet is constructed with two parts. The first part is a spatial transformer module which supports differentiable transformation of images in deep neural network. A convolutional autoencoder (CAE) which encodes dense features follows. The CAE is trained by unsupervised fashion and its features give wide receptive field information to align the source and target images. This method is compared with two other major ssEM image registration methods and increases accuracy and robustness, although it has less number of user parameters.ope

Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks

Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states

Intervertebral Disc Structure and Mechanical Function Under Physiological Loading Quantified Non-invasively Utilizing MRI and Image Registration

The intervertebral discs (IVD) functions to permit motion, distribute load, and dissipate energy in the spine. It performs these functions through its heterogeneous structural organization and biochemical composition consisting of several tissue substructures: the central gelatinous nucleus pulposus (NP), the surrounding fiber reinforced layered annulus fibrosus (AF), and the cartilaginous endplates (CEP) that are positioned between the NP and vertebral endplates. Each tissue contributes individually to overall disc mechanics and by interacting with adjacent tissues. Disruption of the disc\u27s tissues through aging, degeneration, or tear will not only alter the affected tissue mechanical properties, but also the mechanical behavior of adjacent tissues and, ultimately, overall disc segment function. Thus, there is a need to measure disc tissue and segment mechanics in the intact disc so that interactions between substructures are not disrupted. Such measurements would be valuable to study mechanisms of disc function and degeneration, and develop and evaluate surgical procedures and therapeutic implants. The objectives of this study were to develop, validate, and apply methods to visualize and quantify IVD substructure geometry and track internal deformations for intact human discs under axial compression. The CEP and AF were visualized through MRI parameter mapping and image sequence optimization for ideal contrast. High-resolution images enabled geometric measurements. Axial compression was performed using a custom-built loading device that permitted long relaxation times outside of the MRI, 300 m isotropic resolution images were acquired, and image registration methods applied to measure 3D internal strain. In conclusion, new methods to visualize and quantify CEP thickness, annular tear detection and geometric quantification, and non-invasively measure 3D internal disc strains were established. No correlation was found between CEP thickness and disc level; however the periphery was significantly thicker compared to central locations. Clear distinction of adjacent AF lamellae enabled annular tear detection and detailed geometric quantification. Annular tears demonstrated non-classic geometry through interconnecting radial, circumferential, and perinuclear formations. Regional strain inhomogeneity was observed qualitatively and quantitatively. Variation in strain magnitudes might be explained by geometry in axial and circumferential strain while peak radial strain in the posterior AF may have important implications for disc herniation

Differently stained whole slide image registration technique with landmark validation

Abstract. One of the most significant features in digital pathology is to compare and fuse successive differently stained tissue sections, also called slides, visually. Doing so, aligning different images to a common frame, ground truth, is required. Current sample scanning tools enable to create images full of informative layers of digitalized tissues, stored with a high resolution into whole slide images. However, there are a limited amount of automatic alignment tools handling large images precisely in acceptable processing time. The idea of this study is to propose a deep learning solution for histopathology image registration. The main focus is on the understanding of landmark validation and the impact of stain augmentation on differently stained histopathology images. Also, the developed registration method is compared with the state-of-the-art algorithms which utilize whole slide images in the field of digital pathology. There are previous studies about histopathology, digital pathology, whole slide imaging and image registration, color staining, data augmentation, and deep learning that are referenced in this study. The goal is to develop a learning-based registration framework specifically for high-resolution histopathology image registration. Different whole slide tissue sample images are used with a resolution of up to 40x magnification. The images are organized into sets of consecutive, differently dyed sections, and the aim is to register the images based on only the visible tissue and ignore the background. Significant structures in the tissue are marked with landmarks. The quality measurements include, for example, the relative target registration error, structural similarity index metric, visual evaluation, landmark-based evaluation, matching points, and image details. These results are comparable and can be used also in the future research and in development of new tools. Moreover, the results are expected to show how the theory and practice are combined in whole slide image registration challenges. DeepHistReg algorithm will be studied to better understand the development of stain color feature augmentation-based image registration tool of this study. Matlab and Aperio ImageScope are the tools to annotate and validate the image, and Python is used to develop the algorithm of this new registration tool. As cancer is globally a serious disease regardless of age or lifestyle, it is important to find ways to develop the systems experts can use while working with patients’ data. There is still a lot to improve in the field of digital pathology and this study is one step toward it.Eri menetelmin värjättyjen virtuaalinäytelasien rekisteröintitekniikka kiintopisteiden validointia hyödyntäen. Tiivistelmä. Yksi tärkeimmistä digitaalipatologian ominaisuuksista on verrata ja fuusioida peräkkäisiä eri menetelmin värjättyjä kudosleikkeitä toisiinsa visuaalisesti. Tällöin keskenään lähes identtiset kuvat kohdistetaan samaan yhteiseen kehykseen, niin sanottuun pohjatotuuteen. Nykyiset näytteiden skannaustyökalut mahdollistavat sellaisten kuvien luonnin, jotka ovat täynnä kerroksittaista tietoa digitalisoiduista näytteistä, tallennettuna erittäin korkean resoluution virtuaalisiin näytelaseihin. Tällä hetkellä on olemassa kuitenkin vain kourallinen automaattisia työkaluja, jotka kykenevät käsittelemään näin valtavia kuvatiedostoja tarkasti hyväksytyin aikarajoin. Tämän työn tarkoituksena on syväoppimista hyväksikäyttäen löytää ratkaisu histopatologisten kuvien rekisteröintiin. Tärkeimpänä osa-alueena on ymmärtää kiintopisteiden validoinnin periaatteet sekä eri väriaineiden augmentoinnin vaikutus. Lisäksi tässä työssä kehitettyä rekisteröintialgoritmia tullaan vertailemaan muihin kirjallisuudessa esitettyihin algoritmeihin, jotka myös hyödyntävät virtuaalinäytelaseja digitaalipatologian saralla. Kirjallisessa osiossa tullaan siteeraamaan aiempia tutkimuksia muun muassa seuraavista aihealueista: histopatologia, digitaalipatologia, virtuaalinäytelasi, kuvantaminen ja rekisteröinti, näytteen värjäys, data-augmentointi sekä syväoppiminen. Tavoitteena on kehittää oppimispohjainen rekisteröintikehys erityisesti korkearesoluutioisille digitalisoiduille histopatologisille kuville. Erilaisissa näytekuvissa tullaan käyttämään jopa 40-kertaista suurennosta. Kuvat kudoksista on järjestetty eri menetelmin värjättyihin peräkkäisiin kuvasarjoihin ja tämän työn päämääränä on rekisteröidä kuvat pohjautuen ainoastaan kudosten näkyviin osuuksiin, jättäen kuvien tausta huomioimatta. Kudosten merkittävimmät rakenteet on merkattu niin sanotuin kiintopistein. Työn laatumittauksina käytetään arvoja, kuten kohteen suhteellinen rekisteröintivirhe (rTRE), rakenteellisen samankaltaisuuindeksin mittari (SSIM), sekä visuaalista arviointia, kiintopisteisiin pohjautuvaa arviointia, yhteensopivuuskohtia, ja kuvatiedoston yksityiskohtia. Nämä arvot ovat verrattavissa myös tulevissa tutkimuksissa ja samaisia arvoja voidaan käyttää uusia työkaluja kehiteltäessä. DeepHistReg metodi toimii pohjana tässä työssä kehitettävälle näytteen värjäyksen parantamiseen pohjautuvalle rekisteröintityökalulle. Matlab ja Aperio ImageScope ovat ohjelmistoja, joita tullaan hyödyntämään tässä työssä kuvien merkitsemiseen ja validointiin. Ohjelmointikielenä käytetään Pythonia. Syöpä on maailmanlaajuisesti vakava sairaus, joka ei katso ikää eikä elämäntyyliä. Siksi on tärkeää löytää uusia keinoja kehittää työkaluja, joita asiantuntijat voivat hyödyntää jokapäiväisessä työssään potilastietojen käsittelyssä. Digitaalipatologian osa-alueella on vielä paljon innovoitavaa ja tämä työ on yksi askel eteenpäin taistelussa syöpäsairauksia vastaan