The following master's thesis paper equipped with a short description of CT scans and MR images and the main differences between them, explanation of the structure of convolutional neural networks and how they implemented into biomedical image analysis, besides it was taken a popular modification of U-Net and tested on two loss-functions. As far as segmentation quality plays a highly important role for doctors, in experiment part it was paid significant attention to training quality and prediction results of the model. The experiment has shown the effectiveness of the provided algorithm and performed 100 training cases with the following analysis through the similarity. The proposed outcome gives us certain ideas for future improving the quality of image segmentation via deep learning techniques.

A dose prediction model for treatment planning was generated using U-Net architecture. The model was generated for advanced stage cancer patients. The U- Net architecture was created with depth=6 and kernel=6. The model architecture was successful to reduce the input image size (192X192) to feature map (6X6) which helped to extract the low level features. The dose prediction of the model was trained with depth=6, kernel=6, MSE loss, Adam optimizer, 1000 epochs and a batch size of 4. The predicted dose was rescaled for gamma analysis to quantify accuracy of the model. The renormalized predicted dose was quantified using gamma analysis with a 3mm, 3% dose tolerance. The gamma map was generated to visualize the regions where dose distributions failed. The gamma percentage values obtained on the training set were acceptable. The mean and standard deviation values of gamma pass percentage obtained on training dataset were 97.5% and 1.24% respectively, which concluded that training process was successful and was an almost perfect match of true dose and predicted dose. However, gamma pass percentage values obtained on validation set was not a good representation of the true dose. Nevertheless, the validation dataset was able to predict the approximate highest dose region. A gamma analysis with a 5mm, 5% dose tolerance was performed to test the the level of discrepancy between the predicted and true dose in the validation set. This increased the gamma pass percentage compared to the 3mm, 3% analysis to a mean gamma pass percentage of 26.2 ± 7.47%. Although the predicted dose was not of sufficient accuracy for clinical use, there technique studied in this work show promise for further development. / Thesis / Master of Science (MSc)

Scoliosis is a common skeletal condition in which a curvature forms along the coronal plane of the spine. Although scoliosis has been long recognized, its pathophysiology and best mode of treatment are still debated. Currently, definitive diagnosis of scoliosis and its progression are performed through anterior-posterior (AP) radiographs by measuring the angle of coronal curvature, referred to as Cobb angle. Cobb angle measurements can be performed by Deep Learning algorithms and are currently being investigated as a possible diagnostic tool for clinicians. This thesis focuses on the role of Deep Learning in the diagnosis and treatment of Scoliosis and proposes a study design using the algorithms to continue to better understand and classify the disease.

The Portable Document Format (PDF) is an ideal format for viewing and printing documents. Today many companies store their documents in a PDF format. However, the conversion from a PDF document to any other structured format is inherently difficult. As a result, a lot of the information contained in a PDF document is not directly accessible - this is problematic. Manual intervention is required to accurately convert a PDF into another file format - this can be deemed as both strenuous and exhaustive work. An automated solution to this process could greatly improve the accessibility to information in many companies. A significant amount of literature has investigated the process of extracting information from PDF documents in a structured way. In recent years these methodologies have become heavily dependent on computer vision. The work on this paper evaluates how the U-Net model handles multi-label segmentation on PDF documents in a medical context - extending on Stahl et al.’s work in 2018. Furthermore, it compares two newer extensions of the U-Net model, MultiResUNet (2019) and SS-U-Net (2021). Additionally, it assesses how each of the models performs in a data-sparse environment. The three models were implemented, trained, and then evaluated. Their performance was measured using the Dice coefficient, Jaccard coefficient, and percentage similarity. Furthermore, visual inspection was also used to analyze how the models performed from a perceptual standpoint. The results indicate that both the U-Net and the SS-U-Net are exceptional at segmenting PDF documents effectively in a data abundant environment. However, the SS-U-Net outperformed both the U-Net and the MultiResUNet in the data-sparse environment. Furthermore, the MultiResUNet significantly underperformed in comparison to both the U-Net and SS-U-Net models in both environments. The impressive results achieved by the U-Net and SS-U-Net models suggest that it can be combined with a larger system. This proposed system allows for accurate and structured extraction of information from PDF documents. / Portable Document Format (PDF) är ett välfungerande format för visning och utskrift av dokument. I dagsläget väljer många företag därmed att lagra sina dokument i PDF-format. Konvertering från PDF format till någon annan typ av strukturerat format är dock svårt, och detta resulterar i att mycket av informationen i PDF-dokumenten är svårtillgängligt, vilket är problematiskt för de företag som arbetar med detta filformat. Det krävs manuellt arbete för att konvertera en PDF till ett annat filformat - detta kan betraktas som både ansträngande och uttömmande arbete. En automatiserad lösning på denna process skulle kunna förbättra tillgängligheten av information för många företag. En stor mängd litteratur har undersökt processen att extrahera information från PDF-dokument på ett strukturerat sätt. På senare tid har dessa metoder blivit starkt beroende av datorseende. Den här forskningen utvärderar hur U-Net-modellen hanterar segmentering av PDF dokument, baserat på flerfaldiga etiketter, i ett medicinskt sammanhang. Arbetet är en utökning av Stahl et al. forskning från 2018. Dessutom jämförs två nyare utökade varianter av U-Net-modellen , MultiResUNet (2019) och SS-U-Net (2021). Utöver detta så utvärderas även varje modell utefter hur den presterar i en gles datamiljö. De tre modellerna implementerades, utbildades och utvärderades. Deras prestanda mättes med hjälp av Dice-koefficienten, Jaccard-koefficienten och procentuell likhet. Vidare så görs även en visuell inspektion för att analysera hur modellerna presterar utifrån en perceptuell synvinkel. Resultaten tyder på att både U-Net och SS-U-Net är exceptionella när det gäller att segmentera PDF-dokument i en riklig datamiljö. SS-U-Net överträffade emellertid både U-Net och MultiResUNet i den glesa datamiljön. Dessutom underpresterade MultiResUNet signifikant i jämförelse med både U-Net och SS-U-Net modellen i båda miljöerna. De imponerande resultaten som uppnåtts av modellerna U-Net och SS-U-Net tyder på att de kan kombineras med ett större system. Detta föreslagna systemet möjliggör korrekt och strukturerad extrahering av information från PDF-dokument.

Deep convolutional neural networks (CNN’s) have achieved state-of-the-art accuraciesfor multi-class segmentation in biomedical image science. In this thesis, A 3D U-Net isused to segment 4D flow Magnetic Resonance Images that include the heart and its largevessels. The 4 dimensional flow MRI dataset has been segmented and validated using amulti-atlas based registration technique. This multi-atlas based technique resulted in highquality segmentations, with the disadvantage of long computation times typically requiredby three-dimensional registration techniques. The 3D U-Net framework learns to classifyvoxels by transforming the information about the segmentation into a latent feature spacein a contracting path and upsampling them to semantic segmentation in an expandingpath. A CNN trained using a sufficiently diverse set of volumes at different time intervalsof the diastole and systole should be able to handle more extreme morphological differencesbetween subjects. Evaluation of the results is based on metric for segmentation evaluationsuch as Dice coefficient. Uncertainty is estimated using a bayesian implementationof the 3D U-Net of similar architecture. / <p>The presentation was online over zoom due to covid19 restrictions.</p>

The structure of the ankle joint complex creates an ideal balance between mobility and stability, which enables gait. If a lesion emerges in the ankle joint complex, the anatomical structure is altered, which may disturb mobility and stability and cause intense pain. A lesion in the articular cartilage on the talus bone, or a lesion in the subchondral bone of the talar dome, is referred to as an Osteochondral Lesion of the Talus (OLT). Replacing the damaged cartilage or bone with an implant is one of the methods that can be applied to treat OLTs. Episurf Medical develops and produces patient-specific implants (Episealers) along with the necessary associated surgical instruments by, inter alia, creating a corresponding 3D model of the ankle (talus, tibial, and fibula bones) based on either a Magnetic Resonance Imaging (MRI) scan or a Computed Tomography (CT) scan. Presently, the3D models based on MRI scans can be created automatically, but the 3Dmodels based on CT scans must be created manually, which can be very time-demanding. In this thesis project, a U-net based Convolutional Neural Network (CNN) was trained to automatically segment 3D models of ankles based on CT images. Furthermore, in order to optimize the quality of the incoming CT images, this thesis project also consisted of an evaluation of the specified parameters in the Episurf CT talus protocol that is being sent out to the clinics. The performance of the CNN was evaluated using the Dice Coefficient (DC) with five-fold cross-validation. The CNN achieved a mean DC of 0.978±0.009 for the talus bone, 0.779±0.174 for the tibial bone, and 0.938±0.091 for the fibula bone. The values for the talus and fibula bones were satisfactory and comparable to results presented in previous researches; however, due to background artefacts in the images, the DC achieved by the network for the segmentation of the tibial bone was lower than the results presented in previous researches. To correct this, a noise-reducing filter will be implemented. / Fotledens komplexa anatomi ger upphov till en ideal balans mellan rörlighetoch stabilitet, vilket i sin tur möjliggör gång. Fotledens anatomi förändras när en skada uppstår, vilket kan påverka rörligheten och stabiliteten samt orsaka intensiv smärta. En skada i talusbenets ledbrosk eller i det subkondrala benet på talusdomen benämns som en Osteochondral Lesion of the Talus(OLT). En metod att behandla OLTs är att ersätta den del brosk eller bensom är skadat med ett implantat. Episurf Medical utvecklar och producerar individanpassade implantat (Episealers) och tillhörande nödvändiga kirurgiska instrument genom att, bland annat, skapa en motsvarande 3D-modell av fotleden (talus-, tibia- och fibula-benen) baserat på en skanning med antingen magnetisk resonanstomografi (MRI) eller datortomografi (CT). I dagsläget kan de 3D-modeller som baseras på MRI-skanningar skapas automatiskt, medan de 3D-modeller som baseras på CT-skanningar måste skapas manuellt - det senare ofta tidskrävande. I detta examensarbete har ett U-net-baserat Convolutional Neuralt Nätverk (CNN) tränats för att automatiskt kunna segmentera 3D-modeller av fotleder baserat på CT-bilder. Vidare har de speciferade parametrarna i Episurfs CT-protokoll för fotleden som skickas ut till klinikerna utvärderats, detta för att optimera bildkvaliteten på de CT-bilder som används för implantatspositionering och design. Det tränade nätverkets prestanda utvärderades med hjälp av Dicekoefficienten (DC) med en fem-delad korsvalidering. Nätverket åstadkom engenomsnittlig DC på 0.978±0.009 för talusbenet, 0.779±0.174 för tibiabenet, och 0.938±0.091 för fibulabenet. Värdena för talus och fibula var adekvata och jämförbara med resultaten presenterade i tidigare forskning. På grund av bakgrundsartefakter i bilderna blev den DC som nätverket åstadkom för sin segmentering av tibiabenet lägre än tidigiare forskningsresultat. För att korrigera för bakgrundsartefakterna kommer ett brusreduceringsfilter implementeras

<p dir="ltr">Progress in deep learning methodologies has transformed the landscape of medical image analysis, opening fresh pathways for precise and effective diagnostics. Currently, multi-phase liver CT scans follow a four-stage process, commencing with an initial scan carried out before the administration of <a href="" target="_blank">intravenous (IV) contrast-enhancing material</a>. Subsequently, three additional scans are performed following the contrast injection. The primary objective of this research is to automate the analysis and prediction of 50% of liver CT scans. It concentrates on discerning patterns of intensity change during the second, third, and fourth phases concerning the initial phase. The thesis comprises two key sections. The first section employs the non-contrast phase (first scan), late hepatic arterial phase (second scan), and portal venous phase (third scan) to predict the delayed phase (fourth scan). In the second section, the non-contrast phase and late hepatic arterial phase are utilized to predict both the portal venous and delayed phases. The study evaluates the performance of two deep learning models, U-Net and U²-Net. The process involves preprocessing steps like subtraction and normalization to compute contrast difference images, followed by post-processing techniques to generate the predicted 2D CT scans. Post-processing steps have similar techniques as in preprocessing but are performed in reverse order. Four fundamental evaluation metrics, including <a href="" target="_blank">Mean Absolute Error (MAE), Signal-to-Reconstruction Error Ratio (SRE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), </a>are employed for assessment. Based on these evaluation metrics, U²-Net performed better than U-Net for the prediction of both portal venous (third) and delayed (fourth) phases. Specifically, U²-Net exhibited superior MAE and PSNR results for the predicted third and fourth scans. However, U-Net did show slightly better SRE and SSIM performance in the predicted scans. On the other hand, for the exclusive prediction of the fourth scan, U-Net outperforms U²-Net in all four evaluation metrics. This implementation shows promising results which will eliminate the need for additional CT scans and reduce patients’ exposure to harmful radiation. Predicting 50% of liver CT volumes will reduce exposure to harmful radiation by half. The proposed method is not limited to liver CT scans and can be applied to various other multi-phase medical imaging techniques, including multi-phase CT angiography, multi-phase renal CT, contrast-enhanced breast MRI, and more.</p>

Current disaster response strategies are based on damage assessments carried out on the ground, which can be dangerous following a ä destructive event. Damage assessments can also be performed remotely using satellite imagery, but are usually carried out through visual interpretation, which can take a lot of time. This thesis explored a way of using artificial intelligence to automate remote damage assessment. We implemented a dual-task U-Net deep learning model, trained it with the xBD dataset for assessing building damage, and applied the model to pre- and post-event very high resolution satellite imagery of the February 6, 2023 earthquakes in Turkey. The results were compared to damage maps produced using a traditional object based method by calculating the F1 scores associated with the outputs of each method and ground truth data that we compiled. The study areas were parts of the two cities Kahramanmaraş and Antakya. The deep learning model almost only correctly identified undamaged buildings, achieving F1 scores of 0.95 during training as well as 0.93 and 0.83 in the damage assessments of Kahramanmaras and Antakya, respectively. For the other damage classes, the best result was the classification of destroyed buildings, both in training and in the study areas, with a F1-score of 0.45 in training and 0.16 in Kahramanmaraş. The deep learning model performed similarly to the object based method. Although the thesis did not yield good damage maps in the areas of interest, it had many limitations, and there is still a lot of potential for deep learning models to be useful in building damage assessment.

Segmentation of ultrasonic images is a common task in healthcare that requires time and attention from healthcare professionals. Automation of medical image segmentation using deep learning solutions is fast growing field and has been shown to be capable of near human performance. Ultrasonic images suffer from low signal-to-noise ratio and speckle patterns, noise filtering is a common pre-processing step in non-deep learning image segmentation methods used to improve segmentation results. In this thesis the effect of speckle filtering of echocardiographic images in deep learning segmentation using U-Net is investigated. When trained with speckle reduced and despeckled datasets, a U-Net model with 0.5·106 trainable parameters saw an rage dice score improvement of +0.15 in the 17 out of 32 categories that were found to be statistically different compared to the same network trained with unfiltered images. The U-Net model with 1.9·106 trainable parameters saw a decrease in performance in only 5 out of 32 categories, and the U-Net model with 31·106 trainable parameters saw a decrease in performance in 10 out of 32 categories when trained with the speckle filtered datasets. No definite differences in performance between the use of speckle suppression and full speckle removal were observed. This result shows potential for speckle filtering to be used as a means to reduce the complexity required of deep learning models in ultrasound segmentation tasks. The use of the wavelet transform as a down- and up-sampling layer in U-Net was also investigated. The speckle patterns in ultrasonic images can contain information about the tissue. The wavelet transform is capable of lossless down- and up-sampling in contrast to the commonly used down-sampling methods, which could enable the network to make use textural information and improve segmentations. The U-Net modified with the wavelet transform shows slightly improved results when trained with despeckled datasets compared to the unfiltered dataset, suggesting that it was not capable of extracting any information from the speckle. The experiments with the wavelet transform were far from exhaustive and more research is needed for proper assessment. / Segmentering av ultraljudsbilder är en vanlig uppgift inom vården som kräver tid och uppmärksamhet från vårdpersonal. Automatisering av medicinsk bildsegmentering med djupinlärning är ett snabbt växande område och har visat kunna nå prestanda nära mänsklig nivå.  Ultraljudsbilder har dålig signal-brusförhållande och speckle mönster, ofta bearbetas bilder med brusfiltrering när icke djupinlärningsmetoder används för segmentering för att förbättra resultat. Effekten av speckle-filtrering i ultraljudsbilder i djupinlärnings segmentering med U-Net undersöks i den här masterexamensuppsatsen.   U-Net nätverket med 0.5·106 träningsbara parametrar presterade bättre när den tränades med speckle filtrerade dataset jämfört för med ofiltrerade bilder, men en ökning i dice-koefficienten av +0.15 i medel i de 17 kategorier av 32 som var statistikst signifikanta. En försämring av resultaten för U-Net nätverket med 1.9·106 träningsbara parametrar observerades i 5 av 32 kategorier, och en försämring av resultaten för U-Net nätverket med 31·106 träningsbara parametrar observerardes när de tränades med speckle filtrerade dataset i 10 av 32 kategorier. Inga skillnader i prestanda mellan användning av minskning av speckle och fullständig speckle borttagning observerades. Detta resultat visar att det finns potential för att använda speckle filtrering som en metod för att minska komplexiteten som kan krävas hos djupinlärningsnätverk inom ultraljudssegmentering. Användning av wavelet transformen som ett ned- och uppsamplings lager i U-Net undersöktes också. Speckle mönstren i ultraljudsbilder kan innehålla information om vävnaden. Wavelet transformen möjliggör ned- och uppsamplings av bilden utan informationsförlust till skillnad från de vanliga metoderna, vilket skulle kunna göra det möjligt för nätverket att utnyttja information om vävnadstexturen och förbättra segmenteringarna. U-Net nätverket som modifierades med wavelet transformen visar någorlunda bättre prestanda när den tränas med speckle filtrerade dataset jämfört med ofiltrerade dataset. Det tyder på att nätverket inte kunde utnyttja någon information från speckle mönstren. Wavelet transform experimenten var ej uttömmande och mer forskning behövs för en korrekt bedömning.

Manual analysis of medical images such as magnetic resonance imaging (MRI) requires a trained professional, is time-consuming and results may vary between experts. We propose an automated method for kidney segmentation using a convolutional Neural Network (CNN) model based on the U-Net architecture. Investigations are done to compare segmentations between trained experts, inexperienced operators and the Neural Network model, showing near human expert level performance from the Neural Network. Stratified sampling is performed when selecting which subject volumes to perform manual segmentations on to create training data. Experiments are run to test the effectiveness of transfer learning and data augmentation and we show that one of the most important components of a successful machine learning pipeline is larger quantities of carefully annotated data for training.