Segmentation of pathological liver tissue with Dilated Fully Convolutional Networks: A Preliminary Study

Early detection of liver cancer, whether from primary occurrence or from metastization is highly important to establish informed treatment decisions. Accurate delineation of the liver tissues of interest facilitates quantitative assessment of the regions of interest, treatment application, and prognosis. Segmentation of the liver in Computer Tomography (CT) images allows the extraction of the three-dimensional (3D) structure of the liver tissues in which the observation of their relative position to one another is particularly important in treatment scenarios of radiation therapy or interventional surgery planning. The adequate receptive field for the segmentation of such a big organ in CT images, from the remaining neighbouring organs was very successfully improved by the use of the state-of-the-art Convolutional Neural Networks (CNN) algorithms, however, certain issues still arise and are highly dependent of pre-or post-processing methods to refine the final segmentations. Here, the effects of Dilated Convolutional Networks is proposed, for the purpose of improving segmentation of liver tissues in CT. The introduction of a dilation module allowed the concatenation of feature maps with a richer contextual information. The hierarchical learning process given by different dilated convolutional layers is analysed quantitatively. Experiments on the MICCAI Lits challenge dataset are described achieving segmentations with a mean Dice coefficients of 95.57% and 59.36% for the liver and liver tumour, using a total number 30 CT test volumes. (c) ENBENG 2019. All Rights Reserved

Segmentation of tongue shapes during vowel production in magnetic resonance images based on statistical modelling

Quantification of the anatomic and functional aspects of the tongue is pertinent to analyse the mechanisms involved in speech production. Speech requires dynamic and complex articulation of the vocal tract organs, and the tongue is one of the main articulators during speech production. Magnetic resonance imaging has been widely used in speech-related studies. Moreover, the segmentation of such images of speech organs is required to extract reliable statistical data. However, standard solutions to analyse a large set of articulatory images have not yet been established. Therefore, this article presents an approach to segment the tongue in two-dimensional magnetic resonance images and statistically model the segmented tongue shapes. The proposed approach assesses the articulator morphology based on an active shape model, which captures the shape variability of the tongue during speech production. To validate this new approach, a dataset of mid-sagittal magnetic resonance images acquired from four subjects was used, and key aspects of the shape of the tongue during the vocal production of relevant European Portuguese vowels were evaluated

Quantum Fluctuation Relations for the Lindblad Master Equation

An open quantum system interacting with its environment can be modeled under suitable assumptions as a Markov process, described by a Lindblad master equation. In this work, we derive a general set of fluctuation relations for systems governed by a Lindblad equation. These identities provide quantum versions of Jarzynski-Hatano-Sasa and Crooks relations. In the linear response regime, these fluctuation relations yield a fluctuation-dissipation theorem (FDT) valid for a stationary state arbitrarily far from equilibrium. For a closed system, this FDT reduces to the celebrated Callen-Welton-Kubo formula

Pyramid Dilated Residual Pooling Convolutional Network for whole liver segmentation

Segmentation of the liver in Computer Tomography (CT) images allows the extraction of three-dimensional (3D) structure of the liver structure. The adequate receptive field for the segmentation of such a big organ in CT images, from the remaining neighboring organs was very successfully improved by the use of the state-of-the-art Convolutional Neural Networks (CNN) algorithms, however, certain issue still arise and are highly dependent of pre- or post- processing methods to refine the final segmentations. Here, an Encoder-Decoder Dilated Poling Convolutional Network (EDDP) is proposed, composed of an Encoder, a Dilation and a Decoder modules. The introduction of a dilation module has produced allowed the concatenation of feature maps with a richer contextual information. The hierarchical learning process of such feature maps, allows the decoder module of the model to have an improved capacity to analyze more internal pixel areas of the liver, with additional contextual information, given by different dilation convolutional layers. Experiments on the MICCAI Lits challenge dataset are described achieving segmentations with a mean Dice coefficient of 95.7%, using a total number 30 CT test volumes