Fig 1 - uploaded by Anton Osokin
Content may be subject to copyright.
Source publication
The paper describes a method of fully automatic 3D-reconstruction of a mouse brain from a sequence of histological coronal
2D slices. The model is constructed via non-linear transformations between the neighboring slices and further morphing. We
also use rigid-body transforms in the preprocessing stage to align the slices. Afterwards, the obtained...
Context in source publication
Citations
... Despite the importance of histology, a branch of biology focused on the study of the microscopic anatomy of cells and tissues, it is surprising to check that most literature devoted to histological image processing and analysis is focused on registration and 3D reconstruction of the whole brain: 3D reconstruction from a sequence of histological coronal 2D slices using a model built by non-linear transformations between the neighbouring slices [191]; image registration combining the high-frequency components of slice-to-slice histology registration with the low-frequency components of the histology-to-MRI registration [192]; a 2D to 3D nonlinear registration using a registration technique, based on partial differential equations, driven by a local normalized-mutual-information similarity measure [193]; slice-by-slice segmentation of anatomical structures where the successful segmentation of one section provides a prior for the subsequent one [194] (they assume that the segmentation of few sparsely sampled slices is done manually, so it is not a completely automatic method). In [195], the segmentation is treated as a classification problem using RF and Markov Random Fields, which refine the results at the pixel level, but the method requires previous knowledge about the reference slice associated to that image. ...
This PhD dissertation is focused on the development of algorithms for the automatic segmentation of anatomical structures in biomedical images, usually the hippocampus in histological images from the mouse brain. Such algorithms are based on computer vision techniques and artificial intelligence methods. More precisely, on the one hand, we take advantage of deformable models to segment the anatomical structure under consideration, using prior knowledge from different sources, and to embed the segmentation into an optimization framework. On the other hand, metaheuristics and classifiers can be used to perform the optimization of the target function defined by the shape model (as well as to automatically tune the system parameters), and to refine the results obtained by the segmentation process, respectively. Three new different methods, with their corresponding advantages and disadvantages, are described and tested. A broad theoretical discussion, together with an extensive introduction to the state of the art, has also been included to provide an overview necessary for understanding the developed methods.
... Desembocando en sofisticados estudios posteriores que impulsan investigaciones sobre el impacto de los genes en el desarrollo del sistema nervioso en diferentes especies y cómo se mantienen señales moleculares sugiriendo vínculo evolutivo y complejas interacciones para la diferenciación de las especies (Lein et al., 2007 ). Así mismo, las herramientas informáticas que permiten visualizar , procesar e interpretar miles de datos producidos por compañías nacionales y privadas han dado luces sobre nuevos campos como la bioinformática (Davis & Eddy, 2009; Hochheiser & Yanowitz, 2007, Osokin, Vetrov & Kropotov, 2009 Yu & Lee, 2011). A nivel clínico, se han identificado interacciones a nivel molecular que pueden explicar enfermedades, como por ejemplo , sinergia entre genes que explican la participación de varios genes o grupos de genes en la misma operación genética. ...
Los estudios de mapeo genético han influido directamente en áreas biológicas, clínicas
e incluso sociales y filosóficas. Dentro de los principales alcances se encuentran: la secuenciación
del genoma humano completo; comparación y relación molecular con otras
especies; rastreo de poblaciones y cambios adaptativos entre especies; replanteamiento
de conceptos en biología, psicología e incluso filosofía; desarrollo de métodos de control
del comportamiento a partir de la interacción genómica de especies; y desarrollo para
acceso, entendimiento y manipulación de datos de forma inmediata. Concomitante a estos
alcances han surgido profundas limitaciones como lo son: poca asociación clínica entre
genes y enfermedades o condiciones; la inversión de grandes cantidades de dinero público
y privado para enfermedades poco representativas; generación de falsos positivos; algunas
limitaciones en las explicaciones, sugiriendo un abandono del determinismo genético; la
custodia y protección del anonimato de los participantes en los estudios y finalmente, y
quizá la oposición más importante que se escapa al objetivo académico de la ciencia, los
intereses económicos particulares que se encuentran detrás del desarrollo de estos estudios
genéticos. A modo de reflexión final, no es posible pensar en abandonar este tipo de estudios
pero sí reconocer las implicaciones que a futuro se generan.
... Regarding the first category, much work relates to histological image segmentation, registration and reconstruction techniques, among which we could mention: 3D reconstruction from a sequence of histological coronal 2D slices using a model built by non-linear transformations between the neighbouring slices [17]; image registration combining the high-frequency components of slice-to-slice histology registration with the low-frequency components of the histologyto-MRI registration [22]; a 2D to 3D nonlinear registration using a PDE-based registration technique driven by a local normalized-mutual-information similarity measure [9]; slice-by-slice segmentation of anatomical structures where the successful segmentation of one section provides a prior for the subsequent one [19]. ...
The Allen Brain Atlas (ABA) is a cellular-resolution, genome-wide map of gene expression in the mouse brain which allows users to compare gene expression patterns in neuroanatomical structures. The correct localization of the structures is the first step to carry on this comparison in an automatic way. In this paper we present a completely automatic tool for the localization of the hippocampus that can be easily adapted also to other subcortical structures. This goal is achieved in two distinct phases. The first phase, called "best reference slice selection", is performed by comparing the image of the brain with a reference Atlas provided by ABA using a two-step affine registration. By doing so the system is able to automatically find to which brain section the image corresponds and wherein the image the hippocampus is roughly located. The second phase, the proper "hippocampus localization", is based on a method that combines Particle Swarm Optimization (PSO) and a novel technique inspired by Active Shape Models (ASMs). The hippocampus is found by adapting a deformable model derived statistically, in order to make it overlap with the hippocampus image. Experiments on a test set of 120 images yielded a perfect or good localization in 89.2% of cases.
... At last we transfer the anatomic segmentation of the virtual slice to the experimental slice using a non-linear transformation. For the 3D model we update our previous result [5] by adding a non-linear correction of atlas slices (section 2.1). This correction improves the quality of the 3D model significantly. ...
... As a 3D-model of a mouse brain we use the one from [5]. It was constructed from the set of images from Allen Brain Atlas (ABA). ...
... Finally these deformations were used to fill in the gaps between the atlas slices using morphing transform. The details of the algorithm are given in [5]. Here we have modified the algorithm by adding one more step -a non- linear correction of atlas slices. ...
We consider the problem of statistical analysis of gene expression in a mouse brain during cognitive processes. In particular
we focus on the problems of anatomical segmentation of a histological brain slice and estimation of slice’s gene expression
level. The first problem is solved by interactive registration of an experimental brain slice into 3D brain model constructed
using Allen Brain Atlas. The second problem is solved by special image filtering and further smart resolution reduction. We
also describe the procedure of non-linear correction of atlas slices which improves the quality of the 3D-model significantly.
Microscopy is widely used for brain research because of its high resolution and ability to stain for many different biomarkers. Since whole brains are usually sectioned for tissue staining and imaging, reconstruction of 3D brain volumes from these sections is important for visualization and analysis. Recently developed tissue clearing techniques and advanced confocal microscopy enable multilayer sections to be imaged without compromising the resolution. However, noticeable structure inconsistence occurs if surface layers are used to align these sections. In this paper, a structure-based intensity propagation method is designed for the robust representation of multilayer sections. The 3D structures in reconstructed brains are more consistent using the proposed methods. Experiments are conducted on 367 multilayer sections from 20 mouse brains. The average reconstruction quality measured by the structure consistence index increases by 45% with the tissue flattening method, and 29% further with the structure-based intensity propagation.
