Blar i Department of Electrical and Computer Engineering (TN-IDE) på forfatter "Engan, Kjersti"
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The devil is in the details: Whole Slide Image acquisition and processing for artifacts detection, color variation, and data augmentation: A review.
Kanwal, Neel; Pérez-Bueno, Fernando; Schmidt, Arne; Engan, Kjersti; Molina, Rafael (Peer reviewed; Journal article, 2022)Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis of different types of cancer. The preparation and digitization of histological tissues leads to the introduction of artifacts and ... -
Diagnosis, Localization, and Prognosis of Melanoma in WSIs with a Complete Pipeline by Digital Pathology
Benjaminsen, Edvin; Bø-Sande, Marie (Master thesis, 2023)The most dangerous and aggressive form of skin cancer is melanoma, responsible for 90% of skin cancer mortality. Early detection of melanoma plays a crucial role in the prognostic outcome. The diagnostic has to be performed ... -
Extracting coronary arteries in angiographic images
Moene, Alexander Kvale (Masteroppgave/UIS-TN-IDE/2017;, Master thesis, 2017-07-15)This master thesis has as propose to extract coronary arteries form angiographic video. With several stability issues with the easy to implement established functions in the time domain and other methods that is far too ... -
Feature extraction for exploring infarcted regions in perfusion CT images of the brain
Hovland, Eivind (Masteroppgave/UIS-TN-IDE/2018;, Master thesis, 2018-06-15)In Norway, over 15 000 people suffer from acute cerebral stroke annually, it is the leading cause of adult long-term severe disability and a significant reason for admission to nursing homes. In Norway it is a prominent ... -
Generalized vs Specialized activity recognition system for newborn resuscitation videos using Deep Neural Networks.
Aboaja, Chukwudi (Master thesis, 2021)Birth asphyxia is a global problem which has resulted in a high mortality rate of newborn babies all over the globe, it is a newborn’s inability to establish breathing at birth. A notable breakthrough is the marrying of ... -
Image Processing and Deep Neural Networks for Detection of Immune Cells on Histological Images of Bladder Cancer
Svendsen, Fredrik (Masteroppgave/UIS-TN-IDE/2019;, Master thesis, 2019-06)Bladder cancer is the tenth most common cancer type, where urothelial carcinoma is the most common type of bladder cancer. Bladder cancer has been classified as the most expensive type of cancer per patient, as the need ... -
Image processing on histopathological images of urothelial carcinoma – assessment of immune cells
Malkenes, Ørjan (Masteroppgave/UIS-TN-IDE/2018;, Master thesis, 2018-06-15)Bladder cancer is the 6th most common cancer in the world, where urothelial carcinoma is the most common one. Bladder cancer is one of the most economically expensive cancers to treat, as follow up is needed over a long ... -
Invasive cancerous area detection in non-muscle invasive bladder cancer whole slide images
Fuster Navarro, Saul; Khoraminia, Farbod; Kiraz, Umay; Kanwal, Neel; Kvikstad, Vebjørn; Eftestøl, Trygve Christian; Zuiverloon, Tahlita C M; Janssen, Emiel; Engan, Kjersti (Chapter, 2022)Bladder cancer patients’ stratification into risk groups relies on grade, stage and clinical factors. For non-muscle invasive bladder cancer, T1 tumours that invade the subepithelial tissue are high-risk lesions with a ... -
Machine learning algorithms vs. thresholding to segment ischemic regions in patients with acute ischemic stroke
Tomasetti, Luca; Høllesli, Liv Jorunn; Engan, Kjersti; Kurz, Kathinka Dæhli; Kurz, Martin Wilhelm; Khanmohammadi, Mahdieh (Peer reviewed; Journal article, 2021-07)Objective: Computed tomography (CT) scan is a fast and widely used modality for early assessment in patients with symptoms of a cerebral ischemic stroke. CT perfusion (CTP) is often added to the protocol and is used by ... -
Machine learning, unsupervised learning and stain normalization in digital nephropathology
Jon Tveit (Master thesis, 2023)Chronic kidney disease is a serious health challenge and still, the field of study lacks awareness and funding. Improving the efficiency of diagnosing chronic disease is important. Machine learning can be used for various ... -
Melanoma Diagnosis and Localization from Whole Slide Images using Convolutional Neural Networks
Amundsen, Roger (Master thesis, 2022)During the last decade, no other cancer type in Norway have had higher increase in incidents than skin cancer. Melanoma is the most aggressive type of skin cancer because it has the ability to rapidly spread, which makes ... -
Melanoma prognosis prediction using image processing and machine learning
Andreassen, Christopher (Master thesis, 2022)Death of melanoma cancer is most common in Europe, and northern Europe has the second highest mortality rate of melanoma in the world, with 1.9 per 100 000 dying from melanoma in northern Europe in 2020. Prognosis of ... -
Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection
Tomasetti, Luca; Khanmohammadi, Mahdieh; Engan, Kjersti; Høllesli, Liv Jorunn; Kurz, Kathinka Dæhli (Peer reviewed; Journal article, 2022-03)Time is a fundamental factor during stroke treatments. A fast, automatic approach that segmentsthe ischemic regions helps treatment decisions. In clinical use today, a set of color-coded parametric maps generated from ... -
A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides
Wetteland, Rune; Engan, Kjersti; Eftestøl, Trygve Christian; Janssen, Emiel; Kvikstad, Vebjørn (Peer reviewed; Journal article, 2020)In pathology labs worldwide, we see an increasing number of tissue samples that need to be assessed without the same increase in the number of pathologists. Computational pathology, where digital scans of histological ... -
Myocardial Segmentation in LGE-CMR Images Using Deep Neural Networks
Kregnes, Anders (Masteroppgave/UIS-TN-IDE/2019;, Master thesis, 2019-07-29)Cardiovascular diseases are the number one cause of death globally. 85% of these deaths are related to acute myocardial infarction or stroke. One of the methods that are used to diagnose patients affected by myocardial ... -
NewbornTime - improved newborn care based on video and artificial intelligence - study protocol
Engan, Kjersti; Meinich-Bache, Øyvind; Brunner, Sara; Myklebust, Helge; Rong, Chunming; Garcia-Torres Fernandez, Jorge; Ersdal, Hege Langli; Johannessen, Anders; Pike, Hanne; Rettedal, Siren (Journal article, 2023)Background Approximately 3-8% of all newborns do not breathe spontaneously at birth, and require time critical resuscitation. Resuscitation guidelines are mostly based on best practice, and more research on newborn ... -
Optimization of object detection in newborn resuscitation videos
Austnes, Simon (Masteroppgave/UIS-TN-IDE/2019;, Master thesis, 2019-06-14)99% of deaths of children under 28 days of age takes place in low- and middle-income countries. Most of these deaths are due to complications during childbirth. Newborn resuscitation heavily revolves around ventilation, ... -
Probability mapping of scarred myocardium using texture and intensity features in CMR images
Kotu, Lasya Priya; Engan, Kjersti; Skretting, Karl; Måløy, Frode; Ørn, Stein; Woie, Leik; Eftestøl, Trygve (Journal article, 2013) -
Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide Images
Kanwal, Neel; Fuster Navarro, Saul; Khoraminia, Farbod; Zuiverloon, Tahlita C M; Chunming, Rong; Engan, Kjersti (Chapter, 2022)Histological tissue examination has been a longstanding practice for cancer diagnosis where pathologists identify the presence of tumors on glass slides. Slides acquired from laboratory routine may contain unintentional ... -
Safer Births - Using Deep Neural Networks on Fetal Heart Rate Signals
Berntsen, Stian (Masteroppgave/UIS-TN-IDE/2019;, Master thesis, 2019-06)Infant death is a big issue, especially in Africa and parts of Asia where between 24 and 30 [21] in every thousand do not survive the first month. In Europe this number is only 5.9 in every thousand. Reading fetal heart ...