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The main purpose of this Master Thesis is to investigate how front-facing, train-mounted cameras and Computer Vision, a type of Artificial Intelligence (AI), can be used to compensate for GPS inaccuracies. By using footage from track-recording cameras, Computer Vision can be utilized to determine the number of tracks and the track occupancy of the train, which would compensate GPS inaccuracies in the lateral positioning. GPS usage in railway applications is rare, however, an AI-based positioning system would facilitate the usage of GPS for higher capacity and better utilization of current railway infrastructure. This is especially interesting for ERTMS, a European effort to create a standardized signalling system while simultaneously increasing capacity, where potential for an AI-based positioning system can be found in both ERTMS level 2 and level 3. Two Computer Vision models were created, based on two different methods. Images for both models were collected from YouTube videos of train trips recorded with train-mounted cameras. In the first model, the images were labelled according to the number unoccupied adjacent tracks. For example, a left-track occupancy of a double track section would be labelled “01”. The model architecture was based on Convolutional Neural Networks (CNN), a type of AI algorithm specifically developed for image processing, where every pixel in each image was analysed to find patterns corresponding to each label. In the second model, Python software was utilized to manually label every track with bounding boxes. The purpose of the bounding boxes was to demarcate the tracks within the images. Thus, the latter employed strategy did not require the labelling of both the number of tracks and their position. However, it was magnitudes more time consuming. The model was trained using YOLOv3 real-time object detection, a system perfectly fit for real-time track detection. The first model, which was limited to a recognition of up to four tracks, had a 60 % accuracy. The results were adequate considering the unfit method used to train the model and detect tracks. It was not further considered, as the discovery of the second method involving YOLOv3 resulted a more suitable model for the task. The second model was limited to a recognition of up to three tracks due to limited availability of processing power, computer memory, and time. The performance of the second model was evaluated using clips of different track scenarios. In summary, the second model performed well in the following scenarios: · Main-track detection in any environment. · Side-track recognition in simple environments. It performed mediocre in the following scenarios: · Medium-illuminated tunnels. · Tracks seen through windscreens obscured by water droplets. · Side-track detection in complex environments. It performed poorly in the following scenarios: · Low-illuminated tunnels. · Bright tunnel exits. · Side-track detection in snowy conditions. In conclusion, it is possible to create a computer-vision model for track recognition. Although the results presented in this thesis are promising in certain scenarios, the image dataset is far too limited. Only approximately 350 labelled images were available for the model training. To develop a full-scale AI-based positioning system, many more images must be used to fully encapsule all the possible track scenarios. Furthermore, numerous technical specifications must be defined for the development of such a large-scale system, such as camera type (normal, thermal, event-based, lidar etc.), system design, safety analysis, system evaluation strategy etc. Nevertheless, if the development of an AI-based positioning system is successful, it can transition to become a future full-scale railway system of autonomous freight, passenger, and shunting operations. Syftet med denna Masteruppsats är att undersöka hur hyttmonterade tågkameror tillsammans med datorseende, en typ av Artificiell Intelligens (AI), kan användas för att kompensera bristande GPS-positionering. Genom att använda kameror som kontinuerligt filmar framförvarande räls från tåghytten, kan datorseende utnyttjas för att avgöra antalet spår vid tågets aktuella position, samt vilket spår tåget belägger, och på så sätt kompensera eventuella GPS-felaktigheter i tågets laterala position. Användning av GPS inom järnvägens signalsystem är sällsynt, då tekniken inte är särskilt beprövad i signaltekniska syften. Det skulle emellertid kunna främjas genom att introducera ett AI-baserat signalsystem i syfte att höja järnvägens kapacitet och öka utnyttjandegraden av befintliga banor. I synnerhet när det kommer till utvecklingen av ERTMS kan ett AI-baserat signalsystem vara av intresse, då det finns potentiella tillämpningsområden för både ERTMS level 2 och level 3. Två olika modeller för datorseende utvecklades, baserat på två olika metoder. Bildmaterial för båda modellerna togs från YouTube-klipp innehållande inspelningar från hyttmonterade kameror. För den första modellen kategoriserades bilderna i enlighet med antalet fria intilliggande spår. Exempelvis skulle ett vänster-belagt dubbelspår kategoriseras ”01”. Modellarkitekturen baserades på Convolutional Neural Networks (CNN) en AI-algoritm specialanpassad för bland annat bildanalysering, där alla pixlar i varje bild analyserades i syfte att upptäcka mönster mellan bilderna och dess kategori. I den andra modellen användes Python-programvara för att manuellt kategorisera varje spår med en så kallad avgränsningsruta. Avgränsningsrutorna användes i syfte att urskilja spåren från resterande del av bilderna. Kategorisering i enlighet med antalet fria intilliggande spår krävdes således inte, däremot ökade tidsåtgången för kategorisering markant. Modellen tränades med ”YOLOv3 real-time object detection”, ett verktyg speciellt framtaget för bildanalys i realtid, vilket passade perfekt för spårdetektering i realtid. Den första modellen begränsades till igenkänning av maximalt fyra spår och hade 60 % precision. Resultatet var tillfredsställande med tanke på den olämpliga modell som användes för träning samt detektering av spår. Utvecklingen av denna modell avstannades då ett bättre system för spårdetektering baserat på YOLOv3 upptäcktes. Den andra modellen, baserad på YOLOv3 begränsades till igenkänning av maximalt tre spår, då tillgängligheten av processorkraft, lagringsutrymme och tid var begränsad. Precisionen av denna modell bedömdes med hjälp av korta klipp på olika spårmiljöer. Sammanfattningsvis presterade modellen bra i följande scenarion: · Huvudspårsdetektering i alla typer av miljöer · Sidospårsdetektering i enkla miljöer Modellen presterade mediokert i följande scenarion: · Spårdetektering i halvmörka tunnlar · Vindrutor täckta med vattendroppar · Sidospårsdetektering i avancerade miljöer Modellen presterade mycket dåligt i följande scenarion: · Spårdetektering i mörka tunnlar. · Spårdetektering i ljusa tunnelportaler. · Sidospårsdetektering i snöförhållanden. Ur detta kan slutsatsen dras att det är fullt möjligt att skapa ett signalsystem baserat på datorseende. Fastän de resultat som presenterats i denna masteruppsats tyder på god prestanda i vissa scenarion, kan det inte uteslutas att storleken på det använda bildmaterialet inte är adekvat. Endast cirka 350 kategoriserade bilder användes. För en fullskalig utveckling av ett AI-baserat positioneringssystem måste många fler bilder tas i beaktning, för att säkerställa en inkludering av så många spårmiljöer som möjligt. Dessutom måste åtskilliga tekniska specifikationer definieras, så som kameratyp (vanlig kamera, värmekamera, eventbaserad kamera, Lidar osv.), systemdesign, säkerhetsanalys, systemutvärderingsstrategi osv. Om däremot utvecklingen av ett AI-baserat signalsystem lyckas, kan det vidareutvecklas till ett framtida fullskaligt signalsystem för autonoma passagerar- och godståg samt autonoma rangerlok.

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract. They constitute 1-2% of all gastrointestinal neoplasms but are the most common subtype of soft tissue sarcomas, accounting for 20-25%. In the late 1990s, GISTs were more and more recognized as a particular tumor entity. The tumors are supposed to originate from the interstitial pacemaker cells of Cajal. They are usually well circumscribed and can be located in every part of the tubular gastrointestinal tract. Most often GISTs occur in the stomach, followed by the small bowel and colon/rectum. In contrast to epithelial tumors, GISTs grow transmurally and submucosal. GISTs can be found with highly variable growth features including tumors with intraluminal, intra- or transmural, and pedunculated appearance. Here we describe the most common clinical presentation of GISTs on the basis of our 809 patients managed from 2004 to 2017. The median age of our patients was 59 years and the average size of GIST was 75 mm (range: 4 mm to 35 cm). The clinical presentation is very heterogeneous, depending on tumor site, size, and growth pattern. GISTs of the stomach is the group with the lowest rate of acute or emergency symptoms with 31%, followed by GISTs of the duodenum with 42%, whereas GISTs of the small bowel show acute symptoms in more than 50% of the cases and have an emergency surgery rate of almost 15%. Many patients are diagnosed accidentally, through screening examinations, or with latent, unspecific symptoms.

Aim: The purpose of this IRB-approved, retrospective study was to compare image quality between 2D and high-resolution 3D, T2-weighted (T2WI) magnetic resonance imaging (MRI) sequences and to investigate the additional value of ultra-high b-value diffusion-weighted imaging (DWI; b=2,000 mm/s(2)) for both rectal cancer staging and evaluating treatment response. Materials and Methods: From 12 February to 24 August 2016, 26 consecutive patients (22 males, four females; mean age: 61.9 +/- 14.0 years) with histologically-proven rectal cancer. In total 31 examinations [ 12 prior to and 19 after chemoradiation (CRT)] were included. The patients underwent pelvic MRI on a 3.0-T scanner (Magnetom Skyra, Erlangen, Germany). Three radiologists (3, 4, and 5 years of experience in MRI, respectively) independently assessed all images and rated the image quality of DWI (b=800 mm/s(2)), apparent diffusion coefficient map, DWI (b=2,000 mm/s(2)), 3D sagittal T2WI, 3D axial T2WI, 2D sagittal T2WI, and 2D axial T2WI of each patient, respectively. In addition, signal intensity ratios (SIR) were calculated between rectal cancer and obturator internus muscle (background) in all patients after CRT on DWI (b=2,000 mm/s(2)) and correlated with histopathological regression grade (RG). Results: Tumor delineation was significantly better by 2D T2WI than 3D T2WI both before and after CRT (before CRT: Z=-3.2, p=0.02; after CRT: Z=-4.408, pl0.001; all: Z=-5.192; pl0.001) and was the preferred method, although image quality ratings were not significantly different (3D sagittal: 4.00 +/- 0.48; 2D sagittal: 4.03 +/- 0.34, p=0.713; 3D axial: 3.85 +/- 0.61, 2D axial: 3.78 +/- 0.64, p=0.537). Independent t-test showed significantly higher SIR between those with RG 1 or 2 (moderate response: mean score=2.02) and those with RG 3+4 (good response: mean score=0.8) (t=3.044, p=0.011). In those with RG 4 (complete response), SIR of b2000 was 0.946 compared to a 1.41 average of the whole cohort. In two patients, tumor was invisible on b2000 following CRT (RG 3 and 4, respectively). Interobserver agreement was mostly good (kappa=0.6) regarding image quality assessment, except for poor agreement (kappa=0.4) in DWI (b2000) between the two less-experienced readers. Conclusion: In conclusion, 3D T2WI might be useful for evaluating response to neoadjuvant therapy in a comprehensive, cost-effective protocol, where 2D imaging seems to be preferable. In addition, DWI (b2000) may be beneficial in assessing both the primary and the residual tumor after CRT in rectal cancer and SIR may be helpful in assessing response to CRT.

Aim: Prospective comparison of cone beam C-Arm CT based parenchymal liver blood volume (PLBV) and dynamic volume perfusion CT (dVPCT) measurements in patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolisation (TACE) with drug-eluting beads (DEB). Patients and Methods: In 16 patients, changes of PLBV and dVPCT measurements [arterial liver parenchyma (ALP); temporal maximum intensity projection (MIP); hepatic perfusion index (HPI); portal venous parenchyma] were correlated to one another and to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Results: After TACE, the following parameters showed a statistically significant change (p

Purpose Dynamic volume perfusion CT (dVPCT) provides valuable information on tissue perfusion in patients with hepatocellular carcinoma (HCC) and pancreatic cancer. However, currently dVPCT is often performed in addition to conventional CT acquisitions due to the limited morphologic image quality of dose optimized dVPCT protocols. The aim of this study was to prospectively compare objective and subjective image quality, lesion detectability and radiation dose between mean temporal arterial (mTA) and mean temporal portal venous (mTPV) images calculated from low dose dynamic volume perfusion CT (dVPCT) datasets with linearly blended 120-kVp arterial and portal venous datasets in patients with HCC and pancreatic cancer. Materials and methods All patients gave written informed consent for this institutional review board–approved HIPAA compliant study. 27 consecutive patients (18 men, 9 women, mean age, 69.1 years ± 9.4) with histologically proven HCC or suspected pancreatic cancer were prospectively enrolled. The study CT protocol included a dVPCT protocol performed with 70 or 80 kVp tube voltage (18 spiral acquisitions, 71.2 s total acquisition times) and standard dual-energy (90/150 kVpSn) arterial and portal venous acquisition performed 25 min after the dVPCT. The mTA and mTPV images were manually reconstructed from the 3 to 5 best visually selected single arterial and 3 to 5 best single portal venous phases dVPCT dataset. The linearly blended 120-kVp images were calculated from dual-energy CT (DECT) raw data. Image noise, SNR, and CNR of the liver, abdominal aorta (AA) and main portal vein (PV) were compared between the mTA/mTPV and the linearly blended 120-kVp dual-energy arterial and portal venous datasets, respectively. Subjective image quality was evaluated by two radiologists regarding subjective image noise, sharpness and overall diagnostic image quality using a 5-point Likert Scale. In addition, liver lesion detectability was performed for each liver segment by the two radiologists using the linearly blended120-kVp arterial and portal venous datasets as the reference standard. Results Image noise, SNR and CNR values of the mTA and mTPV were significantly higher when compared to the corresponding linearly blended arterial and portal venous 120-kVp datasets (all p Objective image quality of mTA and mTPV were rated significantly better when compared to the linearly blended 120-kVp arterial and portal venous datasets. Both readers were able to detect all liver lesions found on the linearly blended 120-kVp arterial and portal venous datasets using the mTA and mTPV datasets. The effective radiation dose of the dVPCT was 27.6 mSv for the 80 kVp protocol and 14.5 mSv for the 70 kVp protocol. The mean effective radiation dose for the linearly blended 120-kVp arterial and portal venous CT protocol together of the upper abdomen was 5.60 mSv ± 1.48 mSv. Conclusion Our preliminary data suggest that subjective and objective image quality of mTA and mTPV datasets calculated from low-kVp dVPCT datasets is non-inferior when compared to linearly blended 120-kVp arterial and portal venous acquisitions in patients with HCC and pancreatic cancer. Thus, dVPCT could be used as a stand-alone imaging technique without additionally performed conventional arterial and portal venous CT acquisitions.

Laserskanningen har i forhold til andre oppmålings metoder en ekstrem hurtig datainnsamling, men etterbehandlingen er stadig omfatende. Med at velge et relativ stor oppløsning kan område hurtig skannes uten at samtidig å gå i kompromiss med nøyaktigheten ved sammensetningen av modeller. Nøyaktigheten på objekter og flater vil nok alltid vare avhengig av valgt oppløsning. Det er ingen tvil om at laser skanning fra bakke stasjoner er en ny og spennende oppmålings metode som vil vinne fram det kommende år. Spesielt kommer bruks muligheter at utvides med tiden, når/hvis automatisk objekt gjenfinning blir ferdig utviklet og som standard blir en del av programvare. Det ideale skulle også vart vist man kunde bruke en og samme laser både for korte og lange avstander. Når utviklingen kommer så langt, vil en konstruksjon, eksempelvis, ikke vare noen tidsmessig oppgave.