Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring

INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms that are currently in clinical use, such as atlas-based contouring (ABAS), leave room for improvement. The aim of this study was to use a comprehensive evaluation methodology to investigate the performance of HN OAR auto-contouring when using deep learning contouring (DLC), compared to ABAS. METHODS: The DLC neural network was trained on 589 HN cancer patients. DLC was compared to ABAS by providing each method with an independent validation cohort of 104 patients, which had also been manually contoured. For each of the 22 OAR contours - glandular, upper digestive tract and central nervous system (CNS)-related structures - the dice similarity coefficient (DICE), and absolute mean and max dose differences (|Δmean-dose| and |Δmax-dose|) performance measures were obtained. For a subset of 7 OARs, an evaluation of contouring time, inter-observer variation and subjective judgement was performed. RESULTS: DLC resulted in equal or significantly improved quantitative performance measures in 19 out of 22 OARs, compared to the ABAS (DICE/|Δmean dose|/|Δmax dose|: 0.59/4.2/4.1 Gy (ABAS); 0.74/1.1/0.8 Gy (DLC)). The improvements were mainly for the glandular and upper digestive tract OARs. DLC significantly reduced the delineation time for the inexperienced observer. The subjective evaluation showed that DLC contours were more often preferable to the ABAS contours overall, were considered to be more precise, and more often confused with manual contours. Manual contours still outperformed both DLC and ABAS; however, DLC results were within or bordering the inter-observer variability for the manual edited contours in this cohort. CONCLUSION: The DLC, trained on a large HN cancer patient cohort, outperformed the ABAS for the majority of HN OARs

QCD Reggeon Field Theory for every day: Pomeron loops included

We derive the evolution equation for hadronic scattering amplitude at high energy. Our derivation includes the nonlinear effects of finite partonic density in the hadronic wave function as well as the effect of multiple scatterings for scattering on dense hadronic target. It thus includes Pomeron loops. It is based on the evolution of the hadronic wave function derived in \cite{foam}. The kernel of the evolution equation defines the second quantized Hamiltonian of the QCD Reggeon Field Theory, $H_{RFT}$ beyond the limits considered so far. The two previously known limits of the evolution: dilute target (JIMWLK limit) and dilute projectile (KLWMIJ limit) are recovered directly from our final result. The Hamiltonian $H_{RFT}$ is applicable for the evolution of scattering amplitude for arbitrarily dense hadronic projectiles/targets - from "dipole-dipole" to "nucleus-nucleus" scattering processes.Comment: 35 pages, 5 figure

An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning

Atlas-based automatic segmentation is used \nin radiotherapy planning to accelerate the delineation of \norgans at risk (OARs). Atlas selection has been proposed \nas a way to improve the accuracy and execution time of \nsegmentation, assuming that, the more similar the atlas is to \nthe patient, the better the results will be. This paper presents \nan analysis of atlas selection methods in the context of \nradiotherapy treatment planning. For a range of commonly \ncontoured OARs, a thorough comparison of a large class \nof typical atlas selection methods has been performed. For \nthis evaluation, clinically contoured CT images of the head \nand neck (N = 316) and thorax (N = 280) were used. The \nstate-of-the-art intensity and deformation similarity-based \natlas selection methods were found to compare poorly to \nperfect atlas selection. Counter-intuitively, atlas selection \nmethods based on a fixed set of representative atlases \noutperformed atlas selection methods based on the patient \nimage. This study suggests that atlas-based segmentation \nwith currently available selection methods compares poorly \nto the potential best performance, hampering the clinical \nutility of atlas-based segmentation. Effective atlas selection \nremains an open challenge in atlas-based segmentation for \nradiotherapy planning

Probabilistic 3D surface reconstruction from sparse MRI information

Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool should: be fast in prediction of accurate well localised and high resolution models, evaluate prediction uncertainty, work with as little input data as possible. Current deep learning state of the art (SOTA) 3D reconstruction methods, however, often only produce shapes of limited variability positioned in a canonical position or lack uncertainty evaluation. In this paper, we present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets whilst modelling the location of each mesh vertex through a Gaussian distribution. Prior shape information is encoded using a built-in linear principal component analysis (PCA) model. Extensive experiments on cardiac MR data show that our probabilistic approach successfully assesses prediction uncertainty while at the same time qualitatively and quantitatively outperforms SOTA methods in shape prediction. Compared to SOTA, we are capable of properly localising and orientating the prediction via the use of a spatially aware neural network.Comment: MICCAI 202

Signatures of Right-Handed Majorana neutrinos and gauge bosons in eγe \gamma Collisions

The process $e^- \gamma \to e^+ W_R^- W_R^-$ is studied in the framework of the Left-Right symmetric model. It is shown that this reaction and $e^- \gamma \to l^+ W_R^- W_R^-$ for the arbitrary final lepton are likely to be discovered for CLIC collider option. For relatively light doubly charged Higgs boson its mass does not have much influence on the discovery potential, while for heavier values the probability of the reaction increases.Comment: 18 pages, 7 figures, LaTe

Signatures for Majorana neutrinos in e−γe^- \gamma collider

We study the possibilities to detect Majorana neutrinos in $e^- \gamma$ colliders for different center of mass energies. We study the $W^- W^- l_j^{+}(l_j^+\equiv e^+ ,\mu^+ ,\tau^+)$ final state which are, due to leptonic number violation, a clear signature for intermediate Majorana neutrino contribution. Such a signal (final lepton have the opposite charge of the initial lepton) is not possible if the heavy neutrinos are Dirac particles. In our calculation we use the helicity formalism to obtain analytic expressions for the amplitude and we have considered that the intermediate neutrinos can be either on shell or off shell. Finally we present our results for the total cross-section and for the angular distribution of the final lepton. We also include a discussion on the expected events number as a function of the input parameters.Comment: Latex file with 12 pages and 6 figures. Submited to Phys. Rev.