DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images

Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

Cellular automata segmentation of brain tumors on post contrast MR images

In this paper, we re-examine the cellular automata(CA) al- gorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmenta- tion method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Val- idation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type

Parallel matrix inversion techniques

In this paper, we present techniques for inverting sparse, symmetric and positive definite matrices on parallel and distributed computers. We propose two algorithms, one for SIMD implementation and the other for MIMD implementation. These algorithms are modified versions of Gaussian elimination and they take into account the sparseness of the matrix. Our algorithms perform better than the general parallel Gaussian elimination algorithm. In order to demonstrate the usefulness of our technique, we implemented the snake problem using our sparse matrix algorithm. Our studies reveal that the proposed sparse matrix inversion algorithm significantly reduces the time taken for obtaining the solution of the snake problem. In this paper, we present the results of our experimental work

Bi-Plane X-ray coronary 3D reconstruction for flow velocity assessment

In coronary angiography the coronary blood flow velocity can be assessed by tracking a contrast agent in an image sequence. The contrast agent flow velocity is used to estimate the functional behavior of the coronary arteries using TIMI frame count (TFC). In this paper we descibe the 3D reconstruction algorithm used in our method towards the automation of TFC. The method creates a two dimensional map of the contrast agent in which the opacification of the vessel centerline is plotted against time. This map is used to find the velocity of the contrast agent and subsequently the TFC. The vessel centerline is obtained using the Fast Marching Method to find the minimum cost path between\ud the catheter point and the end of the vessel. The determination of the start and endpoint is estimated using a 3D model reconstructed from bi-plane 2D image\ud data. The final reconstructed coronary model only includes the segments matching our error criterion

An evaluation of plume tracking as a strategy for gas source localization in turbulent wind flows

Gas source localization is likely the most direct application of a mobile robot endowed with gas sensing capabilities. Multiple algorithms have been proposed to locate the gas source within a known environment, ranging from bio-inspired to probabilistic ones. However, their application to real-world conditions still remains a major issue due to the great difficulties those scenarios bring, among others, the common presence of obstacles which hamper the movement of the robot and notably ncrease the complexity of the gas dispersion. In this work, we consider a plume tracking algorithm based on the well-known silkworm moth strategy and analyze its performance when facing different realistic environments characterized by the presence of obstacles and turbulent wind flows. We rely on computational fluid dynamics and the open source gas dispersion simulator GADEN to generate realistic gas distributions in scenarios where the presence of obstacles breaks down the ideal downwind plume. We first propose some modifications to the original silkworm moth algorithm in order to deal with the presence of obstacles in the environment (avoiding collisions) and then analyze its performance within four challenging environments.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Proyecto de excelencia de la Junata de Andalucia TEP2012-53

Emergence of Addictive Behaviors in Reinforcement Learning Agents

This paper presents a novel approach to the technical analysis of wireheading in intelligent agents. Inspired by the natural analogues of wireheading and their prevalent manifestations, we propose the modeling of such phenomenon in Reinforcement Learning (RL) agents as psychological disorders. In a preliminary step towards evaluating this proposal, we study the feasibility and dynamics of emergent addictive policies in Q-learning agents in the tractable environment of the game of Snake. We consider a slightly modified settings for this game, in which the environment provides a "drug" seed alongside the original "healthy" seed for the consumption of the snake. We adopt and extend an RL-based model of natural addiction to Q-learning agents in this settings, and derive sufficient parametric conditions for the emergence of addictive behaviors in such agents. Furthermore, we evaluate our theoretical analysis with three sets of simulation-based experiments. The results demonstrate the feasibility of addictive wireheading in RL agents, and provide promising venues of further research on the psychopathological modeling of complex AI safety problems