Evaluering av maskinlæringsmetoder for automatisk tumorsegmentering

The definition of target volumes and organs at risk (OARs) is a critical part of radiotherapy planning. In routine practice, this is typically done manually by clinical experts who contour the structures in medical images prior to dosimetric planning. This is a time-consuming and labor-intensive task. Moreover, manual contouring is inherently a subjective task and substantial contour variability can occur, potentially impacting on radiotherapy treatment and image-derived biomarkers. Automatic segmentation (auto-segmentation) of target volumes and OARs has the potential to save time and resources while reducing contouring variability. Recently, auto-segmentation of OARs using machine learning methods has been integrated into the clinical workflow by several institutions and such tools have been made commercially available by major vendors. The use of machine learning methods for auto-segmentation of target volumes including the gross tumor volume (GTV) is less mature at present but is the focus of extensive ongoing research. The primary aim of this thesis was to investigate the use of machine learning methods for auto-segmentation of the GTV in medical images. Manual GTV contours constituted the ground truth in the analyses. Volumetric overlap and distance-based metrics were used to quantify auto-segmentation performance. Four different image datasets were evaluated. The first dataset, analyzed in papers I–II, consisted of positron emission tomography (PET) and contrast-enhanced computed tomography (ceCT) images of 197 patients with head and neck cancer (HNC). The ceCT images of this dataset were also included in paper IV. Two datasets were analyzed separately in paper III, namely (i) PET, ceCT, and low-dose CT (ldCT) images of 86 patients with anal cancer (AC), and (ii) PET, ceCT, ldCT, and T2 and diffusion-weighted (T2W and DW, respectively) MR images of a subset (n = 36) of the aforementioned AC patients. The last dataset consisted of ceCT images of 36 canine patients with HNC and was analyzed in paper IV. In paper I, three approaches to auto-segmentation of the GTV in patients with HNC were evaluated and compared, namely conventional PET thresholding, classical machine learning algorithms, and deep learning using a 2-dimensional (2D) U-Net convolutional neural network (CNN). For the latter two approaches the effect of imaging modality on auto-segmentation performance was also assessed. Deep learning based on multimodality PET/ceCT image input resulted in superior agreement with the manual ground truth contours, as quantified by geometric overlap and distance-based performance evaluation metrics calculated on a per patient basis. Moreover, only deep learning provided adequate performance for segmentation based solely on ceCT images. For segmentation based on PET-only, all three approaches provided adequate segmentation performance, though deep learning ranked first, followed by classical machine learning, and PET thresholding. In paper II, deep learning-based auto-segmentation of the GTV in patients with HNC using a 2D U-Net architecture was evaluated more thoroughly by introducing new structure-based performance evaluation metrics and including qualitative expert evaluation of the resulting auto-segmentation quality. As in paper I, multimodal PET/ceCT image input provided superior segmentation performance, compared to the single modality CNN models. The structure-based metrics showed quantitatively that the PET signal was vital for the sensitivity of the CNN models, as the superior PET/ceCT-based model identified 86 % of all malignant GTV structures whereas the ceCT-based model only identified 53 % of these structures. Furthermore, the majority of the qualitatively evaluated auto-segmentations (~ 90 %) generated by the best PET/ceCT-based CNN were given a quality score corresponding to substantial clinical value. Based on papers I and II, deep learning with multimodality PET/ceCT image input would be the recommended approach for auto-segmentation of the GTV in human patients with HNC. In paper III, deep learning-based auto-segmentation of the GTV in patients with AC was evaluated for the first time, using a 2D U-Net architecture. Furthermore, an extensive comparison of the impact of different single modality and multimodality combinations of PET, ceCT, ldCT, T2W, and/or DW image input on quantitative auto-segmentation performance was conducted. For both the 86-patient and 36-patient datasets, the models based on PET/ceCT provided the highest mean overlap with the manual ground truth contours. For this task, however, comparable auto-segmentation quality was obtained for solely ceCT-based CNN models. The CNN model based solely on T2W images also obtained acceptable auto-segmentation performance and was ranked as the second-best single modality model for the 36-patient dataset. These results indicate that deep learning could prove a versatile future tool for auto-segmentation of the GTV in patients with AC. Paper IV investigated for the first time the applicability of deep learning-based auto-segmentation of the GTV in canine patients with HNC, using a 3-dimensional (3D) U-Net architecture and ceCT image input. A transfer learning approach where CNN models were pre-trained on the human HNC data and subsequently fine-tuned on canine data was compared to training models from scratch on canine data. These two approaches resulted in similar auto-segmentation performances, which on average was comparable to the overlap metrics obtained for ceCT-based auto-segmentation in human HNC patients. Auto-segmentation in canine HNC patients appeared particularly promising for nasal cavity tumors, as the average overlap with manual contours was 25 % higher for this subgroup, compared to the average for all included tumor sites. In conclusion, deep learning with CNNs provided high-quality GTV autosegmentations for all datasets included in this thesis. In all cases, the best-performing deep learning models resulted in an average overlap with manual contours which was comparable to the reported interobserver agreements between human experts performing manual GTV contouring for the given cancer type and imaging modality. Based on these findings, further investigation of deep learning-based auto-segmentation of the GTV in the given diagnoses would be highly warranted.Definisjon av målvolum og risikoorganer er en kritisk del av planleggingen av strålebehandling. I praksis gjøres dette vanligvis manuelt av kliniske eksperter som tegner inn strukturenes konturer i medisinske bilder før dosimetrisk planlegging. Dette er en tids- og arbeidskrevende oppgave. Manuell inntegning er også subjektiv, og betydelig variasjon i inntegnede konturer kan forekomme. Slik variasjon kan potensielt påvirke strålebehandlingen og bildebaserte biomarkører. Automatisk segmentering (auto-segmentering) av målvolum og risikoorganer kan potensielt spare tid og ressurser samtidig som konturvariasjonen reduseres. Autosegmentering av risikoorganer ved hjelp av maskinlæringsmetoder har nylig blitt implementert som del av den kliniske arbeidsflyten ved flere helseinstitusjoner, og slike verktøy er kommersielt tilgjengelige hos store leverandører av medisinsk teknologi. Auto-segmentering av målvolum inkludert tumorvolumet gross tumor volume (GTV) ved hjelp av maskinlæringsmetoder er per i dag mindre teknologisk modent, men dette området er fokus for omfattende pågående forskning. Hovedmålet med denne avhandlingen var å undersøke bruken av maskinlæringsmetoder for auto-segmentering av GTV i medisinske bilder. Manuelle GTVinntegninger utgjorde grunnsannheten (the ground truth) i analysene. Mål på volumetrisk overlapp og avstand mellom sanne og predikerte konturer ble brukt til å kvantifisere kvaliteten til de automatisk genererte GTV-konturene. Fire forskjellige bildedatasett ble evaluert. Det første datasettet, analysert i artikkel I–II, bestod av positronemisjonstomografi (PET) og kontrastforsterkede computertomografi (ceCT) bilder av 197 pasienter med hode/halskreft. ceCT-bildene i dette datasettet ble også inkludert i artikkel IV. To datasett ble analysert separat i artikkel III, nemlig (i) PET, ceCT og lavdose CT (ldCT) bilder av 86 pasienter med analkreft, og (ii) PET, ceCT, ldCT og T2- og diffusjonsvektet (henholdsvis T2W og DW) MR-bilder av en undergruppe (n = 36) av de ovennevnte analkreftpasientene. Det siste datasettet, som bestod av ceCT-bilder av 36 hunder med hode/halskreft, ble analysert i artikkel IV

Valuation and effective capital funding in startup firms - "To what extent is startup valuation and investment decisions similar or different in venture capital and equity crowdfunding.

This thesis will discusses the similarities and differences between venture capital and equity crowdfunding based on reviewing literature and document analysis. Equity crowdfunding is a method of raising money by soliciting small investments from a large number of people. Venture capitalists are individuals or organizations that provide funding to startup companies in exchange for a share of ownership in the company. Venture capitalists typically place more emphasis on the potential return on investment when making decisions about whether to invest in a startup. This is because they are usually investing other people's money and need to generate a profit for their investors. Equity crowdfunding investors, on the other hand, tend to focus more on the products or services offered by the startup and whether they believe in the long-term viability of the business. The lack of focus on market data and investor experience means that there is a significant knowledge gap between traditional investors and crowd investors. This knowledge gap can be detrimental to the success of a crowdfunding campaign, as it can lead to unrealistic expectations and a lack of understanding of the risks involved. It is important for entrepreneurs to be aware of this knowledge gap and take steps to bridge it, through education and communication with potential investors. To analyze valuation process of the randomly selected companies I used retrieved valuation documents from the Crowdfunding website Folkeinvest. I downloaded all the available valuation documents of 12 companies that had raised money on Folkeinvest from 2021 to 2022. The average valuation of the chosen companies was MNOK 47. The most used valuation method was Discounted Cash Flows (DCF)-valuation. The funding success rate for the companies that used this method was 66\%. This is compared to a funding success rate of 84\% for the companies that used other methods of valuation or a combination of methodology. When comparing this to other valuation methods from the selection of data, we can see that the other methods generate a higher success rate of funding. This results might be connected to the observations of reviewing the available literature in the section about Decision-making - "Crowds are more interested in product development than financial data

Research quality and psychological theory in publications on school shooters with multiple victims : a systematic review of the literature

School shooting homicide events generate considerable attention. A substantial number of research reports have tried to explain the phenomenon. However, the outcome of these studies has produced a conflicting picture of the issue. Our systematic review explored the quality of research in publications on school shooters. Research quality was assessed concerning description of design, method, and interpretation of results according to PRISMA and CRD criteria. We investigated evidence of the impact of psychological theories on how research was designed and interpreted. A total of ten papers met the criteria for inclusion in the review. With a few exceptions the research quality was low. Only three studies contained a separate methods section. Two out of ten studies reported from an interview with a school shooter. Secondary sources such as school, hospital and/or psychological evaluations, were used in four studies, while the rest had only applied tertiary data sources. There was a void of psychological theoretical analysis to inform the creation of relevant research designs. No study discussed psychological theories to inform inference from empirical data to conclusion. Higher quality of research and enhanced focus on theoretical understanding of psychological factors in school shooting are called upon. Keywords: School shootings, homicide, violent crime, psychological theory, literature reviewpublishedVersio

Older people's involvement in activities related to meals in nursing homes

Aims and objectives: To explore how residents in nursing homes perceive their participation in activities related to food and meals, and possible factors influencing their involvement. Background: Eating and drinking are fundamental human needs and consequently essential parts of nursing and nursing care. Therefore and as part of nursing care, encouraging older people in nursing homes to engage in different mealtime activities could be one way to increase participation in activities of daily living and more optimal nutrition status among older people. Design: A cross-sectional survey design was used. Methods. A total of 204 residents (88%) in one Norwegian county agreed to participate and completed a face-to-face interview questionnaire about food and meal experiences. Descriptive and comparative statistics was used. Results: Close to 30% of the residents were vulnerable to malnourishment. None of the residents were involved in menu planning, and more than 90% did not participate in food preparation or setting/clearing tables. Ten per cent were able to choose where they could eat and 5% when they could eat. Older persons living in nursing homes with more than 80 residents and those younger than 65 years of age participated the most, while older people with poor appetites were able to choose more often where they wanted to eat, compared to those with a healthy appetite. Conclusion: The residents in this study appeared to be vulnerable to malnourishment. The results indicated that they only to a limited extent were involved in activities concerning food and meals at the nursing homes. Implications for practice. Management and nurses should focus on residents’ eating and drinking, which are essentials of nursing care. The residents should be asked whether they would like to participate in different mealtime activities. Further, a person-centred care approach that facilitates activities concerning food and meals should be promoted

Menuett

Fourth movement from Suite pour Piano (Op. 20, 1887). Sold by W. Harloff in Bergen. Plate number 1314.https://scholarexchange.furman.edu/krohn-album2/1020/thumbnail.jp

Improving welfare for dairy cows and calves at separation.

The results indicate that both the intensity and extent of the behavioural reaction to separation is alleviated when cow and calf are separated with physical contact. In dairy herds practicing suckling systems, fence-line separation may increase cow and calf welfare compared to separation into pens allowing merely auditory contact

An OCP Compliant Network Adapter for GALS-based SoC Design Using the MANGO Network-on-Chip

The demand for IP reuse and system level scalability in System-on-Chip (SoC) designs is growing. Network-onchip (NoC) constitutes a viable solution space to emerging SoC design challenges. In this paper we describe an OCP compliant network adapter (NA) architecture for the MANGO NoC. The NA decouples communication and computation, providing memory-mapped OCP transactions based on primitive message-passing services of the network. Also, it facilitates GALS-type systems, by adapting to the clockless network. This helps leverage a modular SoC design flow. We evaluate performance and cost of 0.13 µm CMOS standard cell instantiations of the architecture. I

The Human Likeness of Government Chatbots – An Empirical Study from Norwegian Municipalities

While chatbots represent a potentially useful supplement to government information and service provision, transparency requirements imply the need to make sure that this technology is not confused with human support. However, there is a knowledge gap concerning whether and how government chatbots indeed represent a risk of such confusion, in spite of their resemblance with human conversation. To address this gap, we have conducted a study of a Norwegian municipality chatbot including interviews with 16 chatbot users and 18 municipality representatives, as well as analysis of > 2600 citizen dialogues. Interviews with citizen and municipality representatives suggested that citizens typically understood well the chatbot capabilities and limitations, though municipality representatives reported on some examples of humanizing the chatbot in its early phases of deployment. Dialogue analyses indicated that citizens have a markedly utilitarian style in their communication with the chatbot, suggesting limited anthropomorphizing of the chatbot.acceptedVersio

Development of a short form of the questionnaire quality from the patient’s perspective for palliative care (QPP-PC)

Purpose: Patients’ views on quality are important to improve person-centered palliative care. There is a lack of short, validated instruments incorporating patients’ perspectives of the multidisciplinary palliative care services. The aim of this study was to develop a short form of the instrument Quality from the Patient’s Perspective for Palliative Care (QPP-PC) and to describe and compare patients’ perceptions of the subjective importance (SI) of care aspects and their perceptions of care received (PR). Methods: A cross-sectional study was conducted in Norway including 128 patients (67% response rate) in four palliative care contexts. The QPP-PC, based on a person-centered theoretical framework, incorporating the multidisciplinary palliative care, comprises 4 dimensions; medical–technical competence, physical–technical conditions, identity-oriented approach and sociocultural atmosphere, 12 factors (49 items) and 3 single items. The instrument measures SI and PR. Development of the short form of the QPP-PC was inspired by previously published methodological guidelines. Descriptive statistics, paired t-tests, confirmatory factor analysis and Cronbach’s α were used. Results: The short form of QPP-PC consists of 4 dimensions, 20 items and 4 single items. Psychometric evaluation showed a root-mean-square error of approximation (RMSEA) value of 0.109 (SI). Cronbach’s α values ranged between 0.64 and 0.85 for most dimensions on SI scales. Scores on SI and PR scales were mostly high. Significantly higher scores for SI than PR were present for the identity-oriented approach dimension, especially on items about information. Conclusion: RMSEA value was slightly above the recommended level. Cronbach’s α was acceptable for most dimensions. The short form of QPP-PC shows promising results and may be used with caution as an indicator of person-centered patient-reported experience measures evaluating the multidisciplinary palliative care for patients in a late palliative phase. However, the short version of QPP-PC needs to be further validated using new samples of patients.publishedVersio