12 results on '"Skogmo, Hege Kippenes"'
Search Results
2. A 1 bp deletion in HACE1 causes ataxia in Norwegian elkhound, black
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Bellamy, Kim K. L., primary, Skedsmo, Fredrik S., additional, Hultman, Josefin, additional, Arnet, Ellen F., additional, Guttersrud, Ole Albert, additional, Skogmo, Hege Kippenes, additional, Thoresen, Stein Istre, additional, Espenes, Arild, additional, Jäderlund, Karin Hultin, additional, and Lingaas, Frode, additional
- Published
- 2022
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3. DCEMRI of spontaneous canine tumors during fractionated radiotherapy: A pharmacokinetic analysis
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Søvik, Åste, Skogmo, Hege Kippenes, Andersen, Erlend K.F., Bruland, Øyvind S., Olsen, Dag Rune, and Malinen, Eirik
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- 2009
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4. Vertebral column deformity with curved cross‐stitch vertebrae in Norwegian seawater‐farmed Atlantic salmon, Salmo salar L.
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Trangerud, Cathrine, primary, Bjørgen, Håvard, additional, Koppang, Erling Olaf, additional, Grøntvedt, Randi Nygaard, additional, Skogmo, Hege Kippenes, additional, Ottesen, Nina, additional, and Kvellestad, Agnar, additional
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- 2020
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5. Feasibility of contrast-enhanced cone-beam CT for target localization and treatment monitoring
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Rødal, Jan, Søvik, Åste, Skogmo, Hege Kippenes, Knudtsen, Ingerid Skjei, and Malinen, Eirik
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- 2010
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6. The use of bougie‐guided insertion of a laryngeal mask airway device in neonatal piglets after unexpected complications
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Morath, Ute, primary, Skogmo, Hege Kippenes, additional, Ranheim, Birgit, additional, and Levionnois, Olivier Louis, additional
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- 2014
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7. Haemorrhagic pneumonia in sled dogs caused by Streptococcus equi subsp. zooepidemicus - one fatality and two full recoveries: a case report
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Jaeger, Gry, primary, Skogmo, Hege Kippenes, additional, Kolbjørnsen, Øyvor, additional, Larsen, Hans Jørgen Søiland, additional, Bergsjø, Bjarne, additional, and Sørum, Henning, additional
- Published
- 2013
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8. Functional imaging to monitor vascular and metabolic response in canine head and neck tumors during fractionated radiotherapy
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Rødal, Jan, primary, Rusten, Espen, additional, Søvik, Åste, additional, Skogmo, Hege Kippenes, additional, and Malinen, Eirik, additional
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- 2013
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9. Spatiotemporal analysis of tumor uptake patterns in dynamic18FDG-PET and dynamic contrast enhanced CT
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Malinen, Eirik, primary, Rødal, Jan, additional, Knudtsen, Ingerid Skjei, additional, Søvik, Åste, additional, and Skogmo, Hege Kippenes, additional
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- 2011
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10. Spatiotemporal analysis of tumor uptake patterns in dynamic 18FDG-PET and dynamic contrast enhanced CT.
- Author
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Malinen, Eirik, Røødal, Jan, Knudtsen, Ingerid Skjei, Søøvik, ÅÅste, and Skogmo, Hege Kippenes
- Subjects
ANALYSIS of variance ,ANIMAL experimentation ,DEOXY sugars ,DIAGNOSTIC imaging ,DOGS ,COMPUTERS in medicine ,RADIATION doses ,RADIOPHARMACEUTICALS ,RESEARCH funding ,TOMOGRAPHY ,POSITRON emission tomography ,CONTRAST media - Abstract
Background. Molecular and functional imaging techniques such as dynamic positron emission tomography (DPET) and dynamic contrast enhanced computed tomography (DCECT) may provide improved characterization of tumors compared to conventional anatomic imaging. The purpose of the current work was to compare spatiotemporal uptake patterns in DPET and DCECT images. Materials and methods. A PET/CT protocol comprising DCECT with an iodine based contrast agent and DPET with
18 F-fluorodeoxyglucose was set up. The imaging protocol was used for examination of three dogs with spontaneous tumors of the head and neck at sessions prior to and after fractionated radiotherapy. Software tools were developed for downsampling the DCECT image series to the PET image dimensions, for segmentation of tracer uptake pattern in the tumors and for spatiotemporal correlation analysis of DCECT and DPET images. Results. DCECT images evaluated one minute post injection qualitatively resembled the DPET images at most imaging sessions. Segmentation by region growing gave similar tumor extensions in DCECT and DPET images, with a median Dice similarity coefficient of 0.81. A relatively high correlation (median 0.85) was found between temporal tumor uptake patterns from DPET and DCECT. The heterogeneity in tumor uptake was not significantly different in the DPET and DCECT images. The median of the spatial correlation was 0.72. Conclusions. DCECT and DPET gave similar temporal wash-in characteristics, and the images also showed a relatively high spatial correlation. Hence, if the limited spatial resolution of DPET is considered adequate, a single DPET scan only for assessing both tumor perfusion and metabolic activity may be considered. However, further work on a larger number of cases is needed to verify the correlations observed in the present study. [ABSTRACT FROM AUTHOR]- Published
- 2011
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- View/download PDF
11. Evaluering av maskinlæringsmetoder for automatisk tumorsegmentering
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Grøndahl, Aurora Rosvoll, Futsæther, Cecilia Marie, Malinen, Eirik, Tomic, Oliver, Skogmo, Hege Kippenes, Søvik, Åste, and Indahl, Ulf Geir
- Abstract
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.
- Published
- 2023
12. Spatiotemporal analysis of tumor uptake patterns in dynamic (18)FDG-PET and dynamic contrast enhanced CT.
- Author
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Malinen E, Rødal J, Knudtsen IS, Søvik Å, and Skogmo HK
- Subjects
- Adolescent, Adult, Animals, Dogs, Female, Fluorodeoxyglucose F18, Head and Neck Neoplasms radiotherapy, Humans, Male, Prognosis, Radiotherapy Planning, Computer-Assisted, Software, Young Adult, Contrast Media, Head and Neck Neoplasms diagnostic imaging, Head and Neck Neoplasms pathology, Positron-Emission Tomography, Radiopharmaceuticals, Tomography, X-Ray Computed
- Abstract
Background: Molecular and functional imaging techniques such as dynamic positron emission tomography (DPET) and dynamic contrast enhanced computed tomography (DCECT) may provide improved characterization of tumors compared to conventional anatomic imaging. The purpose of the current work was to compare spatiotemporal uptake patterns in DPET and DCECT images., Materials and Methods: A PET/CT protocol comprising DCECT with an iodine based contrast agent and DPET with (18)F-fluorodeoxyglucose was set up. The imaging protocol was used for examination of three dogs with spontaneous tumors of the head and neck at sessions prior to and after fractionated radiotherapy. Software tools were developed for downsampling the DCECT image series to the PET image dimensions, for segmentation of tracer uptake pattern in the tumors and for spatiotemporal correlation analysis of DCECT and DPET images., Results: DCECT images evaluated one minute post injection qualitatively resembled the DPET images at most imaging sessions. Segmentation by region growing gave similar tumor extensions in DCECT and DPET images, with a median Dice similarity coefficient of 0.81. A relatively high correlation (median 0.85) was found between temporal tumor uptake patterns from DPET and DCECT. The heterogeneity in tumor uptake was not significantly different in the DPET and DCECT images. The median of the spatial correlation was 0.72., Conclusions: DCECT and DPET gave similar temporal wash-in characteristics, and the images also showed a relatively high spatial correlation. Hence, if the limited spatial resolution of DPET is considered adequate, a single DPET scan only for assessing both tumor perfusion and metabolic activity may be considered. However, further work on a larger number of cases is needed to verify the correlations observed in the present study.
- Published
- 2011
- Full Text
- View/download PDF
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