Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

<p>Abstract</p> <p>Background</p> <p>Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks.</p> <p>Results</p> <p>We evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from <url>http://cbe.ipk-gatersleben.de</url>.</p> <p>Conclusions</p> <p>The results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.</p

An Unsupervised Learning Model for Deformable Medical Image Registration

We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data. We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest. Given a new pair of scans, we can quickly compute a registration field by directly evaluating the function using the learned parameters. We model this function using a convolutional neural network (CNN), and use a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field. The proposed method does not require supervised information such as ground truth registration fields or anatomical landmarks. We demonstrate registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice. Our method promises to significantly speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is available at https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201

Multimodal Three Dimensional Scene Reconstruction, The Gaussian Fields Framework

The focus of this research is on building 3D representations of real world scenes and objects using different imaging sensors. Primarily range acquisition devices (such as laser scanners and stereo systems) that allow the recovery of 3D geometry, and multi-spectral image sequences including visual and thermal IR images that provide additional scene characteristics. The crucial technical challenge that we addressed is the automatic point-sets registration task. In this context our main contribution is the development of an optimization-based method at the core of which lies a unified criterion that solves simultaneously for the dense point correspondence and transformation recovery problems. The new criterion has a straightforward expression in terms of the datasets and the alignment parameters and was used primarily for 3D rigid registration of point-sets. However it proved also useful for feature-based multimodal image alignment. We derived our method from simple Boolean matching principles by approximation and relaxation. One of the main advantages of the proposed approach, as compared to the widely used class of Iterative Closest Point (ICP) algorithms, is convexity in the neighborhood of the registration parameters and continuous differentiability, allowing for the use of standard gradient-based optimization techniques. Physically the criterion is interpreted in terms of a Gaussian Force Field exerted by one point-set on the other. Such formulation proved useful for controlling and increasing the region of convergence, and hence allowing for more autonomy in correspondence tasks. Furthermore, the criterion can be computed with linear complexity using recently developed Fast Gauss Transform numerical techniques. In addition, we also introduced a new local feature descriptor that was derived from visual saliency principles and which enhanced significantly the performance of the registration algorithm. The resulting technique was subjected to a thorough experimental analysis that highlighted its strength and showed its limitations. Our current applications are in the field of 3D modeling for inspection, surveillance, and biometrics. However, since this matching framework can be applied to any type of data, that can be represented as N-dimensional point-sets, the scope of the method is shown to reach many more pattern analysis applications

Learning to extract features for 2D – 3D multimodal registration

The ability to capture depth information form an scene has greatly increased in the recent years. 3D sensors, traditionally high cost and low resolution sensors, are being democratized and 3D scans of indoor and outdoor scenes are becoming more and more common. However, there is still a great data gap between the amount of captures being performed with 2D and 3D sensors. Although the 3D sensors provide more information about the scene, 2D sensors are still more accessible and widely used. This trade-off between availability and information between sensors brings us to a multimodal scenario of mixed 2D and 3D data. This thesis explores the fundamental block of this multimodal scenario: the registration between a single 2D image and a single unorganized point cloud. An unorganized 3D point cloud is the basic representation of a 3D capture. In this representation the surveyed points are represented only by their real word coordinates and, optionally, by their colour information. This simplistic representation brings multiple challenges to the registration, since most of the state of the art works leverage the existence of metadata about the scene or prior knowledges. Two different techniques are explored to perform the registration: a keypoint-based technique and an edge-based technique. The keypoint-based technique estimates the transformation by means of correspondences detected using Deep Learning, whilst the edge-based technique refines a transformation using a multimodal edge detection to establish anchor points to perform the estimation. An extensive evaluation of the proposed methodologies is performed. Albeit further research is needed to achieve adequate performances, the obtained results show the potential of the usage of deep learning techniques to learn 2D and 3D similarities. The results also show the good performance of the proposed 2D-3D iterative refinement, up to the state of the art on 3D-3D registration.La capacitat de captar informació de profunditat d’una escena ha augmentat molt els darrers anys. Els sensors 3D, tradicionalment d’alt cost i baixa resolució, s’estan democratitzant i escànners 3D d’escents interiors i exteriors són cada vegada més comuns. Tot i això, encara hi ha una gran bretxa entre la quantitat de captures que s’estan realitzant amb sensors 2D i 3D. Tot i que els sensors 3D proporcionen més informació sobre l’escena, els sensors 2D encara són més accessibles i àmpliament utilitzats. Aquesta diferència entre la disponibilitat i la informació entre els sensors ens porta a un escenari multimodal de dades mixtes 2D i 3D. Aquesta tesi explora el bloc fonamental d’aquest escenari multimodal: el registre entre una sola imatge 2D i un sol núvol de punts no organitzat. Un núvol de punts 3D no organitzat és la representació bàsica d’una captura en 3D. En aquesta representació, els punts mesurats es representen només per les seves coordenades i, opcionalment, per la informació de color. Aquesta representació simplista aporta múltiples reptes al registre, ja que la majoria dels algoritmes aprofiten l’existència de metadades sobre l’escena o coneixements previs. Per realitzar el registre s’exploren dues tècniques diferents: una tècnica basada en punts clau i una tècnica basada en contorns. La tècnica basada en punts clau estima la transformació mitjançant correspondències detectades mitjançant Deep Learning, mentre que la tècnica basada en contorns refina una transformació mitjançant una detecció multimodal de la vora per establir punts d’ancoratge per realitzar l’estimació. Es fa una avaluació àmplia de les metodologies proposades. Tot i que es necessita més investigació per obtenir un rendiment adequat, els resultats obtinguts mostren el potencial de l’ús de tècniques d’aprenentatge profund per aprendre similituds 2D i 3D. Els resultats també mostren l’excel·lent rendiment del perfeccionament iteratiu 2D-3D proposat, similar al dels algoritmes de registre 3D-3D.La capacidad de captar información de profundidad de una escena ha aumentado mucho en los últimos años. Los sensores 3D, tradicionalmente de alto costo y baja resolución, se están democratizando y escáneres 3D de escents interiores y exteriores son cada vez más comunes. Sin embargo, todavía hay una gran brecha entre la cantidad de capturas que se están realizando con sensores 2D y 3D. Aunque los sensores 3D proporcionan más información sobre la escena, los sensores 2D todavía son más accesibles y ampliamente utilizados. Esta diferencia entre la disponibilidad y la información entre los sensores nos lleva a un escenario multimodal de datos mixtos 2D y 3D. Esta tesis explora el bloque fundamental de este escenario multimodal: el registro entre una sola imagen 2D y una sola nube de puntos no organizado. Una nube de puntos 3D no organizado es la representación básica de una captura en 3D. En esta representación, los puntos medidos se representan sólo por sus coordenadas y, opcionalmente, por la información de color. Esta representación simplista aporta múltiples retos en el registro, ya que la mayoría de los algoritmos aprovechan la existencia de metadatos sobre la escena o conocimientos previos. Para realizar el registro se exploran dos técnicas diferentes: una técnica basada en puntos clave y una técnica basada en contornos. La técnica basada en puntos clave estima la transformación mediante correspondencias detectadas mediante Deep Learning, mientras que la técnica basada en contornos refina una transformación mediante una detección multimodal del borde para establecer puntos de anclaje para realizar la estimación. Se hace una evaluación amplia de las metodologías propuestas. Aunque se necesita más investigación para obtener un rendimiento adecuado, los resultados obtenidos muestran el potencial del uso de técnicas de aprendizaje profundo para aprender similitudes 2D y 3D. Los resultados también muestran el excelente rendimiento del perfeccionamiento iterativo 2D-3D propuesto, similar al de los algoritmos de registro 3D-3D

Learning to extract features for 2D – 3D multimodal registration

The ability to capture depth information form an scene has greatly increased in the recent years. 3D sensors, traditionally high cost and low resolution sensors, are being democratized and 3D scans of indoor and outdoor scenes are becoming more and more common. However, there is still a great data gap between the amount of captures being performed with 2D and 3D sensors. Although the 3D sensors provide more information about the scene, 2D sensors are still more accessible and widely used. This trade-off between availability and information between sensors brings us to a multimodal scenario of mixed 2D and 3D data. This thesis explores the fundamental block of this multimodal scenario: the registration between a single 2D image and a single unorganized point cloud. An unorganized 3D point cloud is the basic representation of a 3D capture. In this representation the surveyed points are represented only by their real word coordinates and, optionally, by their colour information. This simplistic representation brings multiple challenges to the registration, since most of the state of the art works leverage the existence of metadata about the scene or prior knowledges. Two different techniques are explored to perform the registration: a keypoint-based technique and an edge-based technique. The keypoint-based technique estimates the transformation by means of correspondences detected using Deep Learning, whilst the edge-based technique refines a transformation using a multimodal edge detection to establish anchor points to perform the estimation. An extensive evaluation of the proposed methodologies is performed. Albeit further research is needed to achieve adequate performances, the obtained results show the potential of the usage of deep learning techniques to learn 2D and 3D similarities. The results also show the good performance of the proposed 2D-3D iterative refinement, up to the state of the art on 3D-3D registration.La capacitat de captar informació de profunditat d’una escena ha augmentat molt els darrers anys. Els sensors 3D, tradicionalment d’alt cost i baixa resolució, s’estan democratitzant i escànners 3D d’escents interiors i exteriors són cada vegada més comuns. Tot i això, encara hi ha una gran bretxa entre la quantitat de captures que s’estan realitzant amb sensors 2D i 3D. Tot i que els sensors 3D proporcionen més informació sobre l’escena, els sensors 2D encara són més accessibles i àmpliament utilitzats. Aquesta diferència entre la disponibilitat i la informació entre els sensors ens porta a un escenari multimodal de dades mixtes 2D i 3D. Aquesta tesi explora el bloc fonamental d’aquest escenari multimodal: el registre entre una sola imatge 2D i un sol núvol de punts no organitzat. Un núvol de punts 3D no organitzat és la representació bàsica d’una captura en 3D. En aquesta representació, els punts mesurats es representen només per les seves coordenades i, opcionalment, per la informació de color. Aquesta representació simplista aporta múltiples reptes al registre, ja que la majoria dels algoritmes aprofiten l’existència de metadades sobre l’escena o coneixements previs. Per realitzar el registre s’exploren dues tècniques diferents: una tècnica basada en punts clau i una tècnica basada en contorns. La tècnica basada en punts clau estima la transformació mitjançant correspondències detectades mitjançant Deep Learning, mentre que la tècnica basada en contorns refina una transformació mitjançant una detecció multimodal de la vora per establir punts d’ancoratge per realitzar l’estimació. Es fa una avaluació àmplia de les metodologies proposades. Tot i que es necessita més investigació per obtenir un rendiment adequat, els resultats obtinguts mostren el potencial de l’ús de tècniques d’aprenentatge profund per aprendre similituds 2D i 3D. Els resultats també mostren l’excel·lent rendiment del perfeccionament iteratiu 2D-3D proposat, similar al dels algoritmes de registre 3D-3D.La capacidad de captar información de profundidad de una escena ha aumentado mucho en los últimos años. Los sensores 3D, tradicionalmente de alto costo y baja resolución, se están democratizando y escáneres 3D de escents interiores y exteriores son cada vez más comunes. Sin embargo, todavía hay una gran brecha entre la cantidad de capturas que se están realizando con sensores 2D y 3D. Aunque los sensores 3D proporcionan más información sobre la escena, los sensores 2D todavía son más accesibles y ampliamente utilizados. Esta diferencia entre la disponibilidad y la información entre los sensores nos lleva a un escenario multimodal de datos mixtos 2D y 3D. Esta tesis explora el bloque fundamental de este escenario multimodal: el registro entre una sola imagen 2D y una sola nube de puntos no organizado. Una nube de puntos 3D no organizado es la representación básica de una captura en 3D. En esta representación, los puntos medidos se representan sólo por sus coordenadas y, opcionalmente, por la información de color. Esta representación simplista aporta múltiples retos en el registro, ya que la mayoría de los algoritmos aprovechan la existencia de metadatos sobre la escena o conocimientos previos. Para realizar el registro se exploran dos técnicas diferentes: una técnica basada en puntos clave y una técnica basada en contornos. La técnica basada en puntos clave estima la transformación mediante correspondencias detectadas mediante Deep Learning, mientras que la técnica basada en contornos refina una transformación mediante una detección multimodal del borde para establecer puntos de anclaje para realizar la estimación. Se hace una evaluación amplia de las metodologías propuestas. Aunque se necesita más investigación para obtener un rendimiento adecuado, los resultados obtenidos muestran el potencial del uso de técnicas de aprendizaje profundo para aprender similitudes 2D y 3D. Los resultados también muestran el excelente rendimiento del perfeccionamiento iterativo 2D-3D propuesto, similar al de los algoritmos de registro 3D-3D.Postprint (published version

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest