Multiple RF classifier for the hippocampus segmentation: method and validation on EADC-ADNI harmonized hippocampal protocol

AbstractThe hippocampus has a key role in a number of neurodegenerative diseases, such as Alzheimer's Disease. Here we present a novel method for the automated segmentation of the hippocampus from structural magnetic resonance images (MRI), based on a combination of multiple classifiers. The method is validated on a cohort of 50 T1 MRI scans, comprehending healthy control, mild cognitive impairment, and Alzheimer's Disease subjects. The preliminary release of the EADC-ADNI Harmonized Protocol training labels is used as gold standard. The fully automated pipeline consists of a registration using an affine transformation, the extraction of a local bounding box, and the classification of each voxel in two classes (background and hippocampus). The classification is performed slice-by-slice along each of the three orthogonal directions of the 3D-MRI using a Random Forest (RF) classifier, followed by a fusion of the three full segmentations. Dice coefficients obtained by multiple RF (0.87 ± 0.03) are larger than those obtained by a single monolithic RF applied to the entire bounding box, and are comparable to state-of-the-art. A test on an external cohort of 50 T1 MRI scans shows that the presented method is robust and reliable. Additionally, a comparison of local changes in the morphology of the hippocampi between the three subject groups is performed. Our work showed that a multiple classification approach can be implemented for the segmentation for the measurement of volume and shape changes of the hippocampus with diagnostic purposes

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

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À Revista de Medicina do Departamento Científico do Centro Acadêmico Oswaldo Cruz da Faculdade de Medicina da Universidade de São Paulo

Carta retificando o nome correto dos autores, o trabalho completo e corrigido está publicado no v.84, n.2, p.90-93, 2005

Pancreas-kidney simultaneous transplant with graft derived from previously transplanted dead donor

O aumento das listas de espera por órgãos para transplante faz com que cada vez mais se procure meios de aumentar o “pool” de doadores de órgãos. Para tanto, tem-se utilizado doadores vivos, limítrofes ou sem batimentos cardíacos. Uma outra forma de se  aumentar o número de doadores utilizados é através do uso de órgãos provenientes de doadores previamente transplantados, população que tende a crescer, dado o aumento do número de transplantes realizados no mundo. Os resultados obtidos com esse tipo de doador são favoráveis ao seu uso, demonstrando resultados semelhantes aos obtidos com doadores convencionais. Apresenta-se a seguir o primeiro caso em nosso meio de transplante depâncreas e rim simultâneo com órgãos provenientes de doador que fora submetido a transplante cardíaco.Nota: No vol. 84, n.3-4 de 2005, está publicado a Carta contendo os nomes dos autores deste artigo. O pdf anexo foi corrigido.The raising increase in the patient’s waiting list leaves us search for ways to increase the pool of organ donors. Looking at this, there is the use of living donors, of adjoining donors or non-heart beating donors. Another way to increase the number of donors is through the use of organs that came from previously transplanted donors, a group that is becoming larger, because of the rise of the number of transplants all around the world. The results obtained with this kind of donor are suitable to its use, showing similar results to the obtained with usual donors. We present the first case in our ambience of a simultaneous pancreas and kidney transplant with grafts derived from a heart transplanted donor

Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm

The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice’s index of (Formula presented.) (Formula presented.) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi

Automated Shape Analysis landmarks detection for medical image processing

A fully automated shape analysis algorithm based on the Point Distribution Model is proposed (APoD). The algorithm identifies automatically the edges of noisy shapes, determining for each shape a fixed number of contour points and the underlying true shape. The proposed algorithm has been tested using a database of simulated images with different noise levels. The performance of the model was investigated using 50000 simulated images which differ from a gold standard for approximately 20% of pixels.With this method a Dice index D=0.968±0.004 is obtained. © 2012 Taylor & Francis Group

Random forest classification for hippocampal segmentation in 3D MR images

Main goal of this paper is a detailed analysis of the performances of Random Forest algorithm in the field of automated hippocampalsegmentation using 3D MR Images. Fifty-six T1-weighted whole brain MR images were included in the study, together with the related manually segmented bilateral hippocampi (mask). Firstly, the relationship between manual and automated segmentations of hippocampus was explored using a number of standard metrics. For left (right) hemisphere the Dice's coefficient obtained by RF was 70.6% (68.4%). The structural complexity of 3D MR images is twofold. The amount of voxels per image is huge and the numbers of hippocampus and background voxels are strongly imbalanced. In order to overcome these two limitations, we propose two simple strategies: one consists of filtering the input data using the logical OR of the masks of training images, followed by the RF classification task, the other is constituted by learning the RF classifier plane by plane. Using both strategies, the segmentation performances of RF improve significantly and Dice's coefficients increases up to 79.1% (77.4%) for left (right) sides. © 2013 IEEE

Automated voxel-by-voxel tissue classification for hippocampal segmentation: methods and validation

The hippocampus is an important structural biomarker for Alzheimer's disease (AD) and has a primary role in the pathogenesis of other neurological and psychiatric diseases. This study presents a fully automated pattern recognition system for an accurate and reproducible segmentation of the hippocampus in structural Magnetic Resonance Imaging (MRI). The method was validated on a mixed cohort of 56 T1-weighted structural brain images, and consists of three processing levels: (a) Linear registration: all brain images were registered to a standard template and an automated method was applied to capture the global shape of the hippocampus. (b) Feature extraction: all voxels included in the previously selected volume were characterized by 315 features computed from local information. (c) Voxel classification: a Random Forest algorithm was used to classify voxels as belonging or not belonging to the hippocampus. In order to improve the classification performance, an adaptive learning method based on the use of the Pearson's correlation coefficient was developed. The segmentation results (Dice similarity index = 0.81 ± 0.03) compare well with other state-of-the art approaches. A validation study was conducted on an independent dataset of 100 T1-weighted brain images, achieving significantly better results than those obtained with FreeSurfer

Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm

The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice's index of [Formula: see text] ([Formula: see text]) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi