Fully Automatic Danger Zone Determination for SBRT in NSCLC

Lung cancer is the major cause of cancer death worldwide. The most common form of lung cancer is non-small cell lung cancer(NSCLC). Stereotactic body radiation therapy (SBRT) has emerged as a good alternative to surgery in patients with peripheralstage I NSCLC, demonstrating favorable tumor control and low toxicity. Due to spatial relationship to several critical organs atrisk, SBRT of centrally located lesions is associated with more severe toxicity and requires modification in dose application andfractionation, which is currently evaluated in clinical trials. Therefore a classification of lung tumors into central or peripheralis required. In this work we present a novel, highly versatile, mulitmodality tool for tumor classification which requires no userinteraction. Furthermore the tool can automatically segment the trachea, proximal bronchial tree, mediastinum, gross target volumeand internal target volume. The proposed work is evaluated on 19 cases with different image modalities assessing segmentationquality as well as classification accuracy. Experiments showed a good segmentation quality and a classification accuracy of 95 %.These results suggest the use of the proposed tool for clinical trials to assist clinicians in their work and to fasten up the workflowin NSCLC patients treatment

Policies for an Ageing Workforce Work-life balance, working conditions and equal opportunities 2019

At a time of rapid population ageing, a key means of sustaining current welfare states is to extend the length of working lives. In 2050, the share of people over the age of 75 years will be the same as the share over 65 years today. And just as not all are able to work to the age of 65 now, not everyone will be able to work to the age of 75 in 2050; even if future older workers will in all likelihood be healthier and have better working aids at their disposal. Extending average working lives by 10 years, and at the same time ensuring an adequate social safety net for those unable to work into their late 60s and 70s, is a major social policy challenge for the coming decades. And because people are much more likely to work late in life if they had stable careers before reaching 60, tackling this policy challenge means pulling on many more social policy levers than just pension policy. While being keenly aware of these issues and how they relate to the overall agenda of active ageing, Commissioner Thyssen also reminds us in her Foreword that marked increases in life expectancy – both past and in the future – represent enormous social progress. The Commissioner makes the point that older people too contribute to society. And more so with lifelong learning and investment in skills

Dosimetric Impact of the Positional Imaging Frequency for Hypofractionated Prostate Radiotherapy – A Voxel-by-Voxel Analysis

Background: To investigate deviations between planned and applied treatment doses for hypofractionated prostate radiotherapy and to quantify dosimetric accuracy in dependence of the image guidance frequency. Methods: Daily diagnostic in-room CTs were carried out in 10 patients in treatment position as image guidance for hypofractionated prostate radiotherapy. Fraction doses were mapped to the planning CTs and recalculated, and applied doses were accumulated voxel-wise using deformable registration. Non-daily imaging schedules were simulated by deriving position correction vectors from individual scans and used to rigidly register the following scans until the next repositioning before dose recalculation and accumulation. Planned and applied doses were compared regarding dose-volume indices and TCP and NTCP values in dependence of the imaging and repositioning frequency. Results: Daily image-guided repositioning was associated with only negligible deviations of analyzed dose-volume parameters and conformity/homogeneity indices for the prostate, bladder and rectum. Average CTV T did not significantly deviate from the plan values, and rectum NTCPs were highly comparable, while bladder NTCPs were reduced. For non-daily image-guided repositioning, there were significant deviations in the high-dose range from the planned values. Similarly, CTV dose conformity and homogeneity were reduced. While TCPs and rectal NTCPs did not significantly deteriorate for non-daily repositioning, bladder NTCPs appeared falsely diminished in dependence of the imaging frequency. Conclusion: Using voxel-by-voxel dose accumulation, we showed for the first time that daily image-guided repositioning resulted in only negligible dosimetric deviations for hypofractionated prostate radiotherapy. Regarding dosimetric aberrations for non-daily imaging, daily imaging is required to adequately deliver treatment

Deformation based manual segmentation in three and four dimensions

Zsfassung in dt. SpracheDie Segmentierung von medizinischen 3D und 4D Bilddaten etablierte sich in den letzten Jahren zu einem der wichtigsten Teilgebiete der Medizin. Die Hauptaufgabengebiete der Segmentierung im medizinischen Bereich liegen in der Diagnose, der Simulation und der Planung von Therapien und Operationen. Der Forschungsfokus lag in den letzten Jahren hauptsächlich in der Entwicklung von automatischen und halb-automatischen Segmentierungsalgorithmen. Der Nachteil dieser Algorithmen ist, dass sie auf bestimmte Problemstellungen spezialisiert sind, weil viel Vorwissen für die automatische und halb-automatische Segmentierung notwendig ist und dass die Segmentierungsergebnisse oft nachkorrigiert werden müssen, wenn Pathologien oder andere Abnormalitäten existieren. Eine Alternative zu automatischen und halb-automatischen Methoden bietet die manuelle Segmentierung. Die Nachteile dieses Ansatzes liegen darin, dass er sehr zeitaufwändig und ermüdend ist, dass das Ergebnis sehr stark vom Wissen des Arztes abhängt und dass es sehr schwer ist, Ergebnisse zu reproduzieren.Diese Arbeit präsentiert einen Ansatz, der es ermöglicht, die Ergebnisse von automatischen und halb-automatischen Segmentierungsalgorithmen zu verbessern, sowie eine schnelle manuelle Segmentierung von Objekten willkürlicher Form von Grund auf durchzuführen. Mit Hilfe des Programms, dass im Zuge dieser Arbeit entwickelt wurde, können 3D und 4D Bilddatensätze aller gängigen Bildgebungsverfahren segmentiert werden.Die Segmentierung, basierend auf Dreiecksnetzen, wird anhand eines zweidimensionalen Schnittes durch das Gitternetz durchgeführt, der durch den Benutzer an die zu segmentierenden Form angepasst wird. Im Hintergrund wird dabei allerdings das Gitternetz in allen drei Dimensionen verändert. Um das Gitternetz besser an Kanten anzupassen, wird der Sticky Edges-Algorithmus vorgestellt, der den Anwender in dieser Aufgabe unterstützt. Um eine schnellere Segmentierung von 4D Datensätzen zu ermöglichen, stehen dem Benutzer zwei Methoden zur Verfügung. Mit Ersterer können die Segmentierungsschritte, die an einem Datensatz durchgeführt werden, aufgezeichnet und danach automatisch auf andere Datensätze übertragen werden. Die zweite Methode ermöglich es, ein bereits segmentiertes Gitternetz eines Datensatzes als Ausgangspunkt für die Segmentierung eines anderen Datensatzes zu verwenden. Der Ansatz dieser Arbeit ist bis zu 25 mal schneller, als die Segmentierung mit der Livewire-Methode, die zur Evaluierung herangezogen wurde. Bezüglich Glätte, Krümmung und Dreiecks-Qualität der Gitternetze sind die generierten Ergebnisse auf Augenhöhe mit denen der Evaluierungssoftware.Der durchschnittliche geometrische Abstand zwischen den Gitternetzen der Evaluierungssoftware und dieses Ansatzes beträgt 2 mm. Die Abweichung der Normalen beträgt zwischen 0.3 und 0.4 Grad. Zusammenfassend wird in dieser Arbeit eine Methode präsentiert, die eine schnelle manuelle Segmentierung ermöglicht und Gitternetze von guter Qualität liefert.Segmentation of medical image data has grown into one of the most important parts in medicine during the past years. The main fields of segmentation in the area of medicine are surgical-planning, diagnosis, therapy-planning and simulation. The focus was in the last years mainly on automatic and semi-automatic segmentation methods for 3D and 4D datasets. Most of them are highly specialized as they need a lot of prior knowledge and often the results, especially in presence of pathologies or other abnormalities, have to be corrected manually. An alternative to automatic and semi-automatic methods is to perform the segmentation manually. The main drawbacks of this approach are that it is very tedious and time consuming, the user knowledge has a very high impact on the results and it is very hard to reproduce specific results. This work presents a tool that enables the user to enhance results of automatic and semi-automatic algorithms and to do fast manual segmentation of shapes of arbitrary topology from scratch. The tool can deal with three and four dimensional image datasets captured by different modalities. The segmentation is mesh based and performed with a 2D cut approach. With the use of this approach the user aligns a 2D cut through the mesh to the shape to segment but in the background the 3D mesh gets deformed. To achieve a better alignment of the edges to a specific shape the edge class based Sticky Edges algorithm is introduced. Furthermore, well known mesh optimization algorithms like subdivision, smoothing and decimation were implemented to accomplish better results. To achieve a faster segmentation of four dimensional datasets two methods are presented. With the first one the user can record its interactions on one volume in the 4D dataset and apply them automatically to the other volumes. The other one enables the user set an already segmented mesh as start position for the segmentation of other volumes.The approach presented in this work is up to 25 times faster than the Livewire approach that was used to evaluate this tool. Moreover, the mesh quality regarding smoothness, curvature and triangle quality are at eye level with the evaluation meshes. The geometric distance to the ground truth meshes is on average 2 mm and the normal deviation is between 0.3 and 0.4 degree. To sum up, this master thesis introduces a tool for fast manual image segmentation that provides proper mesh quality.9