Abstract

Abstract

Abstract

Abstract
Radiotherapy planning is a crucial task in cancer patients’ management. This task is, however, very time consuming and prone to a high intra and inter subject variance and human errors. In this way, the present line of work aims at developing a tool to help the specialists in this task. The developed tool will consider the delimitation of anatomical regions of interest, since it is crucial to identify the organs at risk and minimize the exposure of these organs to the radiation. This paper, in particular, presents a lung segmentation algorithm, based on image processing techniques, such as intensity projection and region growing, for Computed Tomography volumes. Our pipeline consists in first separating two halves of the volume to isolate each lung. Then, three techniques for seed placement are developed. Finally, a traditional region growing algorithm has been changed in order to automatically derive the value of the threshold parameter. The results obtained for the three different techniques for seed placement were, respectively, 74%, 74% and 92% of DICE with the Iterative Region Growing algorithm. Although the presented results have as use case the Hodgkin Lymphoma, we believe that the developed method is generalizable to any other pathology. © 2019, Springer Nature Switzerland AG.

Abstract
The analysis of heart sounds is a challenging task, due to the quick temporal onset between successive events and the fact that an important fraction of the information carried by phonocardiogram (PCG) signals lies in the inaudible part of the human spectrum. For these reasons, computer-aided analysis of the PCG can dramatically improve the quantity of information recovered from such signals. In this paper, a hidden semi-Markov model (HSMM) is used to automatically segment PCG signals. In the proposed models, the emission probability distributions are approximated via Gaussian mixture model (GMM) priors. The choice of GMM emission probability distributions allow to apply re-estimation routines to automatically adjust the HSMM emission probability distributions to each subject. Building on the proposed method for fine tuning emission distributions, a novel subject-driven unsupervised heart sound segmentation algorithm is proposed and validated over the publicly available PhysioNet dataset. Perhaps surprisingly, the proposed unsupervised method achieved results in line with state-of-the-art supervised approaches, when applied to long heart sounds.

Abstract
This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. Different temporal modeling schemes are applied to the output of the proposed neural network, which induce the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). In particular, convolutional neural networks are used in conjunction with underlying hidden Markov models and hidden semi-Markov models to infer emission distributions. The proposed approaches are tested on heart sound signals from the publicly available PhysioNet dataset, and they are shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.9 and an average positive predictive value of 94 in detecting S1 and S2 sounds.