In digital pathology, hematoxylin & eosin (H&E) is a routine stain which is performed on most clinical cases and it often provides clinicians with sufficient information for diagnosis. However, when making decisions on how to guide breast cancer treatment, immunohistochemical staining of human epidermal growth factor 2 (HER2 staining) is also needed. Over-expression of the HER2 protein plays a significant role in the progression of breast cancer and is therefore important to consider during treatment planning. However, the downside of HER2 staining is that it is both time consuming and rather expensive. This thesis explores the possibility for H&E-to-HER2 stain translation using generative adversarial networks (GANs). If effective, this has the potential to reduce the costs and time spent on tissue processing while still providing clinicians with the images necessary to make a complete diagnosis. To explore this area two supervised (Pix2Pix, PyramidPix2Pix) and one unsupervised (cycleGAN) GAN structure was implemented and trained on digital pathology images from the MIST dataset. These models were trained two times, with 256x256 and 512x512 patches, to see which effect patch size has on stain translation performance as well. In addition, a methodology for evaluating the quality of the generated HER2 patches was also presented and utilized. This methodology consists of structural similarity index (SSIM) and peak signal to noise ratio (PSNR) comparison to the ground truth, and a HER2 status classification protocol. In the latter a classification tool provided by Sectra was used to assign each patch with a HER2 status of No tumor, 1+, 2+ or 3+ and the statuses of the generated patches were then compared to the statuses of the ground truths. The results show that the supervised Pyramid Pix2Pix model trained on 512x512 patches performs the best according to the SSIM and PSNR metrics. However, the unsupervised cycleGAN model shows more promising results when it comes to both visual assessment and the HER2 status classification protocol. Especially when trained on 256x256 patches for 200 epochs which gave an accuracy of 0.655, F1-score of 0.674 and MCC of 0.490. In conclusion the HER2 status classification protocol is deemed as a suitable way to evaluate H&E-to-HER2 stain translation and thereby the unsupervised method is considered to be better than the supervised. Moreover, it is also concluded that a smaller patch size result in worse translation of cellular structure for the supervised methods. Further studies should focus on incorporating HER2 status classification in the cycleGAN loss function and more extensive training runs to further improve the quality of H&E-to-HER2 stain translation.

No description available.

Genom att generera mer information om hälsenan i form av dess elasticitet kan förhoppningsvis fler slutsater nås gällande diagnostik och behandling. Elastografi med hjälp av ultraljud skulle kunna vara en metod för att bidra med denna information. För att utföra detta anpassades en mjukvara utifrån ett grundläggande basprogram för elastografimätningar, utvecklat av företaget Verasonics, för att kunna utföra elastografi av hälsenan genom programmering i matlab. Tidigare undersökningsmetoder för elastografi är utvecklade för större organ, varför anpassningen innebar att använda metoder som även ger tillförlitlig information för mindre organ. För att göra detta anpassades först mjukvaran för en mindre fantom med liknande djup som hälsenan. När det konstaterats att skjuvningsvågor genererats på rätt avstånd kunde sedan mätningar göras på hälsenan. Genom att bestämma hastigheten av de genererade skjuvningsvågorna kunde sedan skjuvmodulen, följt av elasticitetsmodulen, beräknas för vävnaden. Denna bestämdes först genom grupphastigheten av skjuvningsvågorna, vilket är den metod som används vid större organ, följt av fashastigheten av skjuvningsvågorna vilket tar hänsyn till vågens dispersion. Detta gav då hälsenans elasticitetsmodul enligt grupphastighet samt fashastighet som sedan kunde jämföras. Slutligen gick det att konstatera att elasticitetsmodulen kommer att variera beroende på vilken typ av hastighet denna härleds från. Detta indikerar då på att sjuvningsvågen interagerar med organets gränsyta vilket orsakar dispersion. / Generating more information about the achilles tendon, such as its elasticity, will hopefully lead to more conclusions and results within both diagnostics as well as treatment. Elastography by ultrasound could be a method to contribute with this information. To do so, a basic software,provided and developed by the company Verasonics for elastography was specialized to fit the achilles tendon by programing in matlab. Earlier methods to perform elastography are developed for larger organs, hence the adjustment will include methods that acquire trustworthy information from smaller organs. To do so the adjustment of the software was first made to work on a smaller phantom with similar symmetry as the achilles tendon. When it was confirmed that shear waves were generated at the correct distance this enabled further measurements on the achilles tendon. By deciding the speed of the generated shear waves the shear modulus, followed by the elastic modulus, could then be estimated for the tissue. This was first decided by the group velocity of the shear waves, as the usual method done on larger organs, followed by the phase velocity that also takes dispersion in mind. The result could then be used to obtain the elastic modulus of the achilles tendon based on group and phase velocity for further comparison.The conclusion was then that the elastic modulus will depend on what kind of velocity it is derived from. This indicates that the shear wave interacts with the organ's boundaries which causes dispersion.

In the past twenty years, the combination of the advances in medical imaging technologies and therapeutic methods had a great impact in developing minimally invasive interventional procedures. Although the use of medical imaging for the surgery and therapy guidance dates back to the early days of x-ray discovery, there is an increasing evidence in using the new imaging modalities such as computed tomography (CT), magnetic reso- nance imaging (MRI) and ultrasound in the operating rooms. The focus of this thesis is on developing image-guided interventional methods and techniques to support the radiation therapy treatment of gynecologic cancers. Gynecologic cancers which involves malignan- cies of the uterus, cervix, vagina and the ovaries are one of the top causes of mortality and morbidity among the women in U.S. and worldwide. The common treatment plan for radiation therapy of gynecologic cancers is chemotherapy and external beam radiation therapy followed by brachytherapy. Gynecological brachytherapy involves placement of interstitial catheters in and around the tumor area, often with the aid of an applicator. The goal is to create an optimal brachytherapy treatment plan that leads to maximal radiation dose to the cancerous tissue and minimal destructive radiation to the organs at risk. The accuracy of the catheter placement has a leading effect in the success of the treatment. However there are several techniques are developed for navigation of catheters and needles for procedures such as prostate biopsy, brain biopsy, and cardiac ablation, it is obviously lacking for gynecologic brachytherapy procedures. This thesis proposes a technique which aims to increase the accuracy and efficiency of catheter placements in gynecologic brachytherapy by guiding the catheters with an electromagnetic tracking system. To increase the accuracy of needle placement a navigation system has been set up and the appropriate software tools were developed and released for the public use as a module in the open-source 3D Slicer software. The developed technology can be translated from benchmark to the bedside to offer the potential benefit of maximizing tumor coverage during catheter placement while avoiding damage to the adjacent organs including bladder, rectum and bowel. To test the designed system two independent experiments were designed and performed on a phantom model in order to evaluate the targeting accuracy of the tracking system and the mean targeting error over all experiments was less than 2.9 mm, which can be compared to the targeting errors in the available commercial clinical navigation systems.

No description available.

Convolutional neural networks are equivariant to translations; equivariance to other symmetries, however, is not defined and the class output may vary depending on the input's orientation. To mitigate this, the training data can be augmented at the cost of increased redundancy in the model. Another solution is to build an equivariant neural network and thereby increasing the equivariance to a larger symmetry group. In this study, two convolutional neural networks and their respective equivariant counterparts are constructed and applied to the symmetry groups D4 and C8 to explore the impact on performance when removing and adding batch normalisation and data augmentation. The results suggest that data augmentation is irrelevant to an equivariant model and equivariance to more symmetries can slightly improve accuracy. The convolutional neural networks rely heavily on batch normalisation, whereas the equivariant models achieve high accuracy, although lower than with batch normalisation present.

In cancer research, there is a need to make accurate spatial measurements in multi-layered fluorescence microscopy images. Researchers would like to measure distances in and between biological objects such as nerves and tumours, to investigate questions which includes if nerve distribution in and around tumours can have a prognostic value in cancer diagnostics. This thesis is split into two parts, the first being: given arbitrary florescent images of cancer tissue samples, investigate the feasibility of automatically identifying nerves, tumours and blood vessels using classic image analysis. The second part is: given an image with identified objects, quantify their spatial data. By analysing 58 different cancer tissue samples we found that a modified Otsu method gives the most promising results for image segmentation. We found that non-verifiable objects and verifiable objects share the same pixel intensity distributions which implies that it is in general not possible to solely use thresholding methods to separate them from each other. For the spatial analysis, two measurement methods were introduced. An object based method that provides measurements from the edges of nerves to tumour edges, and a pixel based measurement method, which provides fraction based measurements that are comparable between different tissue samples.

Patients who are treated in an intensive care unit need continuous monitoring in orderfor clinicians to be prepared to intervene should a secondary event occur. For patientstreated at the neurointensive care unit (NICU) who have suffered a subarachnoid hemorrhage (SAH) this secondary event could be ischemia, resulting in a lack of blood flow.Blood flow can be measured using magnetic resonance imaging (MRI). The process is facilitated with a software called NOVA. Repeated measurements can therefore be performedas a way to monitor the patients, which in this context would be referred to as longitudinalmeasurements. As more data can be collected ways of analyzing and visualizing the datain a comprehensible way is needed. The aim of this thesis was therefore to develop and implement a method for analyzing and visualizing the longitudinal MR measurement data.With this aim in mind two research questions were relevant. The first one was how NOVAflow longitudinal measurements can be visualized to simplify interpretation by cliniciansand the second one was in what ways the longitudinal data can be analyzed. A graphicaluser interface (GUI) was created to present the developed analysis and visualization tool.Development of the tool progressed using feedback from supervisors and neurosurgeons.Visualization and analysis was done through plots of blood velocity and blood flow as themain component as well as a 2D vessel map. The final implementation showed multipleexamples of how the longitudinal data could be both visualized and analyzed. The resultstherefore provided a tool to analyze and visualize NOVA flow longitudinal measurementsin a way which was easily interpreted. Further improvements of the tool is possible andan area of improvement could involve increasing the adaptability of the tool.

Ultraljudsdiagnostik baseras på propagering av mekaniska vågor och används för att avbilda tvärsnitt av kroppen i realtid. Prestandan, med avseende på kontrast, upplösning och måttmätningar, av CONTEC CMS600B-3, en relativ billig maskin är av intresse. Hur volymen av en fantom, dess ytarea och frekvens på utskickade vågor påverkar uppvärmningen av vävnader är även av intresse. Det undersöktes med ultraljudsmaskinerna CONTEC CMS600B-3 och Philips Lumify för att få resultat som inte beror på endast en maskin.  Axiella upplösningen på CONTEC CMS600B-3 uppmättes med hjälp av ett gem till 0,61 mm och den laterala upplösningen till 1,27 mm med hjälp av ett snitt i cement. Maskinens måttmätningar hade en relativ avvikelse   beroende på mätning. Resultat för reflektionskoefficienten visade att ultraljudsmaskinen har en funktion som kompenserar för attenuering och förstärker signaler med låga amplituder.  Temperaturmätningarna undersöktes genom att skapa fantomer som efterliknar mänskliga vävnader med olika volymer och ytareor. En undersökning med ultraljudsmaskinerna visade att mer temperaturökning sker då ytarean ökas när volymen hålls konstant. Med avseende på säkerhet i temperaturökning, axiell upplösning och area/omkrets mått uppfyller CONTEC CMS600B-3 inte standarden och kan därmed inte användas inom sjukvården. / Diagnostics with ultrasound are based on propagation of mechanical waves and is used for imaging cross-section of the body in real-time. Performance, regarding contrast, resolution, and size measure, of CONTEC CMS600B-3, a relatively cheap machine is of interest. How volume of a phantom, its surface area, and frequency of the waves affects the heating of the tissues is also of interest. It was measured using ultrasound machines CONTEC CMS600B-3 and Philips Lumify to obtain results independent of the machine used.   The axial resolution of CONTEC CMS600B-3 was established with a paperclip to be 0.61 mm and the lateral resolution was measured to be 1.27 mm using concrete with a triangular slit. Measurements of the machine had a relative deviation was   depending on the measure. Results of the reflection coefficient indicated that CONTEC CMS600B-3 has a built-in function that compensates for loss of intensity due to attenuation and amplifies signals with lower amplitude to produce a B-mode image that the user can understand.  Temperature measurements were done on phantoms that mimic the human body with different volumes and surface areas. An investigation with ultrasound machines showed an increase in temperature with increased surface area as the volume is held constant. When looking at safety with temperature rise, axial resolution and area/circumference measurements, CONTEC CMS600B-3 does not meet the standard and therefore cannot be used in healthcare.

Segmentation of the knee cartilage is an important step for surgery planning and manufacturing patient-specific prostheses. What has been a promising technology in recent years is deep learning-based super-resolution methods that are composed of feed-forward models which have been successfully applied on natural and medical images. This thesis aims to test the feasibility to super-resolve thick slice 2D sequence acquisitions and acquire sufficient segmentation accuracy of the articular cartilage in the knee. The investigated approaches are single- and multi-contrast super-resolution, where the contrasts are either based on the 2D sequence, 3D sequence, or both. The deep learning models investigated are based on predicting the residual image between the high- and low-resolution image pairs, finding the hidden latent features connecting the image pairs, and approximating the end-to-end non-linear mapping between the low- and high-resolution image pairs. The results showed a slight improvement in segmentation accuracy with regards to the baseline bilinear interpolation for the single-contrast super-resolution, however, no notable improvements in segmentation accuracy were observed for the multi-contrast case. Although the multi-contrast approach did not result in any notable improvements, there are still unexplored areas not covered in this work that are promising and could potentially be covered as future work. / Segmentering av knäbrosket är ett viktigt steg för planering inför operationer och tillverkning av patientspecifika proteser. Idag segmenterar man knäbrosk med hjälp av MR-bilder tagna med en 3D-sekvens som både tidskrävande och rörelsekänsligt, vilket kan vara obehagligt för patienten. I samband med 3D-bildtagningar brukar även thick slice 2D-sekvenser tas för diagnostiska skäl, däremot är de inte anpassade för segmentering på grund av för tjocka skivor. På senare tid har djupinlärningsbaserade superupplösningsmetoder uppbyggda av så kallade feed-forwardmodeller visat sig vara väldigt framgångsrikt när det applicerats på verkliga- och medicinska bilder. Syftet med den här rapporten är att testa hur väl superupplösta thick slice 2D-sekvensbildtagningar fungerar för segmentering av ledbrosket i knät. De undersökta tillvägagångssätten är superupplösning av enkel- och flerkontrastbilder, där kontrasten är antingen baserade på 2D-sekvensen, 3D-sekvensen eller både och. Resultaten påvisar en liten förbättring av segmenteringnoggrannhet vid segmentering av enkelkontrastbilderna över baslinjen linjär interpolering. Däremot var det inte någon märkvärdig förbättring i superupplösning av flerkontrastbilderna. Även om superupplösning av flerkontrastmetoden inte gav någon märkbar förbättring segmenteringsresultaten så finns det fortfarande outforskade områden som inte tagits upp i det här arbetet som potentiellt skulle kunna utforskas i framtida arbeten.