Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201

Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis

PURPOSE: Accurate measurement of the right ventricle (RV) volume is important for the assessment of the ventricular function and a biomarker of the progression of any cardiovascular disease. However, the high RV variability makes difficult a proper delineation of the myocardium wall. This paper introduces a new automatic method for segmenting the RV volume from short axis cardiac magnetic resonance (MR) images by a salient analysis of temporal and spatial observations. METHODS: The RV volume estimation starts by localizing the heart as the region with the most coherent motion during the cardiac cycle. Afterward, the ventricular chambers are identified at the basal level using the isodata algorithm, the right ventricle extracted, and its centroid computed. A series of radial intensity profiles, traced from this centroid, is used to search a salient intensity pattern that models the inner-outer myocardium boundary. This process is iteratively applied toward the apex, using the segmentation of the previous slice as a regularizer. The consecutive 2D segmentations are added together to obtain the final RV endocardium volume that serves to estimate also the epicardium. RESULTS: Experiments performed with a public dataset, provided by the RV segmentation challenge in cardiac MRI, demonstrated that this method is highly competitive with respect to the state of the art, obtaining a Dice score of 0.87, and a Hausdorff distance of 7.26 mm while a whole volume was segmented in about 3 s. CONCLUSIONS: The proposed method provides an useful delineation of the RV shape using only the spatial and temporal information of the cine MR images. This methodology may be used by the expert to achieve cardiac indicators of the right ventricle function

Segmentation of the right ventricle using diffusion maps and Markov random fields

Abstract. Accurate automated segmentation of the right ventricle is difficult due in part to the large shape variation found between patients. We explore the ability of manifold learning based shape models to rep-resent the complexity of shape variation found within an RV dataset as compared to a typical PCA based model. This is empirically evaluated with the manifold model displaying a greater ability to represent com-plex shapes. Furthermore, we present a combined manifold shape model and Markov Random Field Segmentation framework. The novelty of this method is the iterative generation of targeted shape priors from the man-ifold using image information and a current estimate of the segmentation; a process that can be seen as a traversal across the manifold. We apply our method to the independently evaluated MICCAI 2012 RV Segmen-tation Challenge data set. Our method performs similarly or better than the state-of-the-art methods.

Segmentation of the right ventricle in MRI images using a dual active shape model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166226/1/ipr2bf01366.pd

Definition of the minimal MEN1 candidate area based on a 5-Mb integrated map of proximal 11q13

Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder with a high penetrance characterized by tumors of the parathyroid glands, the endocrine pancreas, and the anterior pituitary. The MEN1 gene, a putative tumor suppressor gene, has been mapped to a 3- to 8-cM region in chromosome 11q13 but it remains elusive as yet. We have combined the efforts and resources from four laboratories to form the European Consortium on MEN1 with the aims of establishing the genetic and the physical maps of 11q13 and of further narrowing the MEN1 region. A 5-Mb integrated map of the region was established by fluorescence in situ hybridization on both metaphase chromosomes and DNA fibers, by hybridization to DNA from somatic cell hybrids containing various parts of human chromosome 11, by long-range restriction mapping, and by characterization of YACs and cosmids. Polymorphic markers were positioned and ordered by physical mapping and genetic linkage in 86 MEN1 families with 452 affected individuals. Two critical recombinants identified in two affected cases placed the MEN1 gene in an ≃2-Mb region around PYGM, flanked by D11S1883 and D11S449

Definition of the minimal MEN1 candidate area based on a 5-Mb integrated map of proximal 11q13. The European Consortium on Men1, (GENEM 1; Groupe d'Etude des Neoplasies Endocriniennes Multiples de type 1).

Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder with a high penetrance characterized by tumors of the parathyroid glands, the endocrine pancreas, and the anterior pituitary. The MEN1 gene, a putative tumor suppressor gene, has been mapped to a 3- to 8-cM region in chromosome 11q13 but it remains elusive as yet. We have combined the efforts and resources from four laboratories to form the European Consortium on MEN1 with the aims of establishing the genetic and the physical maps of 11q13 and of further narrowing the MEN1 region. A 5-Mb integrated map of the region was established by fluorescence in situ hybridization on both metaphase chromosomes and DNA fibers, by hybridization to DNA from somatic cell hybrids containing various parts of human chromosome 11, by long-range restriction mapping, and by characterization of YACs and cosmids. Polymorphic markers were positioned and ordered by physical mapping and genetic linkage in 86 MEN1 families with 452 affected individuals. Two critical recombinants identified in two affected cases placed the MEN1 gene in an approximately 2-Mb region around PYGM, flanked by D11S1883 and D11S449