Atlas-Based Prostate Segmentation Using an Hybrid Registration

Purpose: This paper presents the preliminary results of a semi-automatic method for prostate segmentation of Magnetic Resonance Images (MRI) which aims to be incorporated in a navigation system for prostate brachytherapy. Methods: The method is based on the registration of an anatomical atlas computed from a population of 18 MRI exams onto a patient image. An hybrid registration framework which couples an intensity-based registration with a robust point-matching algorithm is used for both atlas building and atlas registration. Results: The method has been validated on the same dataset that the one used to construct the atlas using the "leave-one-out method". Results gives a mean error of 3.39 mm and a standard deviation of 1.95 mm with respect to expert segmentations. Conclusions: We think that this segmentation tool may be a very valuable help to the clinician for routine quantitative image exploitation.Comment: International Journal of Computer Assisted Radiology and Surgery (2008) 000-99

JULIDE: A Software Tool for 3D Reconstruction and Statistical Analysis of Autoradiographic Mouse Brain Sections

In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. This software tool has been developed in the open-source ITK software framework and is freely available under a GPL license. The article presents the complete image processing chain from raw data acquisition to 3D statistical group analysis. Results of the group comparison in the context of a study on spatial learning are shown as an illustration of the data that can be obtained with this tool

Segmentação de discos intervertebrais lombares para modelação e simulação computacional

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), 2022, Universidade de Lisboa, Faculdade de CiênciasA lombalgia é a principal causa de incapacidade a nível mundial. A degeneração do disco intervertebral é uma das causas da lombalgia, podendo em casos avançados necessitar da remoção do disco intervertebral e substituição deste por um implante. Este implante pode consistir num dispositivo contendo enxerto ósseo (fusão espinhal) ou num disco intervertebral artificial (artroplastia discal). Ambos os métodos apresentam vantagens e desvantagens, pelo que é importante estudar, através de modelação e simulação em elementos finitos, a forma como implantes específicos afetam a biomecânica da coluna lombar antes de os inserir. Esta modelação personalizada requer a capacidade de segmentar as estruturas anatómicas relevantes a partir de imagens médicas. O presente trabalho teve como principal objetivo a implementação/desenvolvimento de um método para localizar e segmentar automaticamente discos intervertebrais lombares em 3D a partir de imagens de ressonância magnética em ponderação T2, com o intuito de auxiliar a construção de modelos de elementos finitos da coluna lombar a partir de casos reais, fornecendo informação precisa e personalizada sobre a forma dos discos intervertebrais do paciente. O desenvolvimento do método para permitir adicionalmente segmentar separadamente as duas principais estruturas do disco – anel fibroso e núcleo pulposo – e detetar automaticamente casos em que a degeneração não permite fazer esta distinção foi posteriormente seguido como objetivo secundário. O método de segmentação foi desenvolvido a partir de um método pré-existente na literatura para realização de segmentações 2D no perfil sagital, tendo este sido parcialmente implementado, modificado e adaptado para uso em 3D. O método permitiu realizar segmentações com uma exatidão média de 87.0 ± 3.7% medida pelo coeficiente de Dice em relação a segmentações manuais de referência. Esta eficácia é comparável com outros métodos de segmentação 3D na literatura. Este método apresenta a vantagem de ser significativamente mais rápido que a maioria dos métodos existentes, demorando apenas alguns segundos para completar uma segmentação dos discos lombares. O método para detetar degeneração discal identificou corretamente o estado de 96% dos discos (saudáveis e degenerados) com que foi testado.Back pain, especially in the lumbar spine, is the main cause of disability in the world. Intervertebral disc (IVD) degeneration is one of the causes of back pain. In some cases this requires the removal of the disc and its replacement with an implant. This implant may consist of either a cage containing bone graft (spinal fusion) or an artificial IVD (disc arthroplasty). Both of these treatments have advantages and disadvantages, which is why it is important to study, through computer modeling and finite element simulation, the ways in which specific implants affect the biomechanics of the lumbar spine before inserting them. This customized modeling requires the ability to segment the relevant anatomical structures from medical images. The present work had as its main objective the implementation/development of a method for localizing and automatically segmenting lumbar IVDs in 3D from T2 weighted magnetic resonance imaging, with the goal of supporting and complementing the generation of finite element models from real lumbar spines, by providing accurate and personalized information on the shape of the patient’s IVDs. The development of the method to also allow performing separate segmentations of the IVD’s two main structures – annulus fibrosus and nucleus pulposus – as well as automatically detecting degenerated IVDs where this distinction is no longer possible was later pursued as a secondary objective. The segmentation method was developed from a pre-existing method in the literature aimed at performing 2D segmentations in the sagittal profile, which was partially implemented, modified and adapted to 3D use. The method performed segmentations with a mean accuracy of 87.0 ± 3.7% as measured by the Dice coefficient in relation to manually segmented reference volumes, or ground truths. This method has the advantage of being significantly faster than most existing 3D segmentation methods, requiring only a few seconds to perform a complete segmentation of the lumbar discs. The method for detecting IVD degeneration correctly identified the status of 96% of the discs (healthy and degenerated) on which it was tested

A Parameter-Efficient Deep Dense Residual Convolutional Neural Network for Volumetric Brain Tissue Segmentation from Magnetic Resonance Images

Brain tissue segmentation is a common medical image processing problem that deals with identifying a region of interest in the human brain from medical scans. It is a fundamental step towards neuroscience research and clinical diagnosis. Magnetic resonance (MR) images are widely used for segmentation in view of their non-invasive acquisition, and high spatial resolution and various contrast information. Accurate segmentation of brain tissues from MR images is very challenging due to the presence of motion artifacts, low signal-to-noise ratio, intensity overlaps, and intra- and inter-subject variability. Convolutional neural networks (CNNs) recently employed for segmentation provide remarkable advantages over the traditional and manual segmentation methods, however, their complex architectures and the large number of parameters make them computationally expensive and difficult to optimize. In this thesis, a novel learning-based algorithm using a three-dimensional deep convolutional neural network is proposed for efficient parameter reduction and compact feature representation to learn end-to-end mapping of T1-weighted (T1w) and/or T2-weighted (T2w) brain MR images to the probability scores of each voxel belonging to the different labels of brain tissues, namely, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) for segmentation. The basic idea in the proposed method is to use densely connected convolutional layers and residual skip-connections to increase representation capacity, facilitate better gradient flow, improve learning, and significantly reduce the number of parameters in the network. The network is independently trained on three different loss functions, cross-entropy, dice similarity, and a combination of the two and the results are compared with each other to investigate better loss function for the training. The model has the number of network parameters reduced by a significant amount compared to that of the state-of-the-art methods in brain tissue segmentation. Experiments are performed using the single-modality IBSR18 dataset containing high-resolution T1-weighted MR scans of diverse age groups, and the multi-modality iSeg-2017 dataset containing T1w and T2w MR scans of infants. It is shown that the proposed method provides the best performance on the test sets of both datasets amongst all the existing deep-learning based methods for brain tissue segmentation using the MR images and achieves competitive performance in the iSeg-2017 challenge with the number of parameters that is 47% to 98% lower than that of the other deep-learning based architectures

Model-based segmentation and registration of multimodal medical images

Ph.DDOCTOR OF PHILOSOPH

Multi-subject Registration for Unbiased Statistical Atlas Construction

This paper introduces a new similarity measure designed to bring a population of segmented subjects into alignment in a common coordinate system. Our metric aligns each subject with a hidden probabilistic model of the common spatial distribution of anatomical tissues, estimated using STAPLE. Our approach does not require the selection of a subject of the population as a “target subject”, nor the identification of “stable” landmarks across subjects. Rather, the approach determines automatically from the data what the most consistent alignment of the joint data is, subject to the particular transformation family used to align the subjects. The computational cost of joint simultaneous registration of the population of subjects is small due to the use of an efficient gradient estimate used to solve the optimization transform aligning each subject. The efficacy of the approach in constructing an unbiased statistical atlas was demonstrated by carrying out joint alignment of 20 segmentations of MRI of healthy preterm infants, using an affine transformation model and a FEM volumetric tetrahedral mesh transformation model