Imiomics – Large-Scale Analysis of Medical Volume Images
Robin Strand, Filip Malmberg
Partner: Joel Kullberg, Håkan Ahlström, Simon Ekström, Therese Sjöholm, Dept. of Surgical Sciences,
Radiology, UU Hospital
Funding: Faculty of Medicine, UU
Period: 20120801–
Abstract: In this project, we mainly process magnetic resonance tomography (MR) images. MR images are very useful in clinical use and in medical research, e.g., for analyzing the composition of the human body.
At the division of Radiology, UU, a huge amount of MR data, including whole body MR images, is acquired for research on the connection between the composition of the human body and disease. To compare volume images voxel by voxel, we develop a large scale analysis method, which is enabled by image registration methods. These methods utilize, for example, segmented tissue and anatomical landmarks. Based on this idea, we have developed Imiomics (imaging omics) – an image analysis concept, including image registration, that allows statistical and holistic analysis of whole-body image data. The Imiomics concept is holistic in three respects: (i) The whole body is analyzed, (ii) All collected image data is used in the analysis and (iii) It allows integration of all other collected non-imaging patient information in the analysis. During 2017 a new registration method based on combinatorial optimization was developed, and a manuscript describing this method was submitted
Interactive Deep Learning Segmentation for Decision Support in Neuroradiology
Ashis Kumar Dhara, Robin Strand, Filip Malmberg
Partner: Johan Wikström and Elna-Marie Larsson, Dept. of Surgical Sciences, Radiology, UU
Funding: Swedish Research Council
Period: 20150501–
Abstract: Many brain diseases can damage brain cells (nerve cells), which can lead to loss of nerve cells and, secondarily, loss of brain volume. Technical imaging advancements allow detection and quantification of very small tissue volumes in magnetic resonance (MR) neuroimaging. Due to the enormous amount of information in a typical MR brain volume scan, and difficulties such as partial volume effects, noise, artefacts, etc., interactive tools for computer aided analysis are absolutely essential for this task. Available interactive methods are often not suited for this problem. Deep learning by convolution neural networks has the ability to learn complex structures from training data. However, deep learning is often too slow for interactive processing. We develop, analyze and evaluate interactive deep learning segmentation methods for quantification and treatment response analysis in neuroimaging. Interaction speed is obtained by dividing the segmentation procedure into an offline pre-segmentation step and an on-line interactive loop in which the user adds constraints until satisfactory result is obtained. The overarching aim is to allow detailed correct diagnosis, as well as accurate and precise analysis of treatment response in neuroimaging, in particular in quantification of intracranial aneurysm remnants and brain tumors (Gliomas WHO-grades III and IV)
growth.
Interactive Segmentation and Analysis of Medical Images
Filip Malmberg, Robin Strand, Ingela Nyström
Partner: Joel Kullberg, Håkan Ahlström, Dept. of Surgical Sciences, UU
Funding: TN-Faculty, UU
Period: 20110601–20170501
Abstract: Three-dimensional (3D) imaging technique such as computed tomography (CT) and magnetic resonance imaging (MRI) are now routinely used in medicine. This has led to an ever increasing flow of high-resolution, high-dimensional, image data that needs to be qualitatively and quantitatively analyzed.
Typically, this analysis requires accurate segmentation of the image. At CBA, we have been developing powerful new methods for interactive image segmentation. In this project, we seek to employ these methods for segmentation of medical images, in collaboration with the Dept. of Surgical Sciences at the UU Hospital. A publicly available software for interactive segmentation, SmartPaint, can be downloaded from urlhttp://www.cb.uu.se/ filip/SmartPaint/. To date, this software has been downloaded more than 1100 times. During 2017, this software was adapted to fit within a workflow for analysis of very large cohorts.
Comparison of Articular Osteochondrosis in Domestic Pigs and Wild Boars by Image Processing
Robin Strand
Partner: Pernille Etterlin, Stina Ekman, Dept. of Biomedical Sciences and Veterinary Public Health, SLU;
Kristin Olstad, Dept. of Companion Animal Clinical Sciences, Norwegian University of Life Sciences;
Charles Ley, Dept. of Clinical Sciences, SLU
Funding: Gerhard Forsells stipendiestiftelse; TN-Faculty, UU
Period: 20150101–
Abstract: Articular osteochondrosis (OC) often develops in typical locations within joints and the characterization of OC distribution in the pig tarsus is incomplete. Prevalence of OC is high in domestic pigs but is presumed to be low in wild boars. In this project, we develop methods based on image registration for 3D analysis of OC distribution. In 2017, a paper was published in the journal Veterinary Pathology.
Methods for Combined MR and Radiation Therapy Equipment
Robin Strand
Partner: Anders Ahnesjö, David Tilly, Dept. of Immunology, Genetics and Pathology, UU. Samuel Fransson,
Håkan Ahlström, Dept. of Surgical Sciences, Radiology, UU
Funding: Vinnova; Barncancerfonden; TN-Faculty, UU
Period: 20160601–
Abstract: UU and Hospital are current investing in image guided radiotherapy. An important component in the strategy is a combined MR scanner and treatment unit, enabling MR imaging right before and during treatment making it possible to adjust for internal motion. In this project, we develop methods for fast detection and quantification of motion for real-time adjustment of the radiation therapy in the combined MR scanner and treatment unit.
Statistical Considerations in Whole-Body MR Analyses
Eva Breznik, Robin Strand, Filip Malmberg
Partner: Joel Kullberg, Håkan Ahlström, Dept. of Surgical Sciences, UU
Funding: Centre for Interdisciplinary Mathematics, CIM, UU; TN-Faculty, UU
Period: 201609–
Abstract: In this project, the focus is on testing and developing methods for Imiomics, to facilitate utilization of whole-body MR images for medical purposes. For inference about activated areas, present in the image, statistical tests are done on series of images at every voxel. This introduces accuracy and reliability problems when drawing conclusions regarding the images or multi-voxel areas as a whole, due to the large number of tests that are considered at the same time. The solution to this problem is a proper multiple testing correction method. Therefore we need to test the existing ones on our specific datasets and explore possibilities of new ones, specifically tailored to our problem. Results have been in part presented at SSBA 2017 in Linköping.
Abdominal Organ Segmentation
Eva Breznik, Robin Strand, Filip Malmberg
Partner: Joel Kullberg, Håkan Ahlström, Division of Radiology, Dept. of Surgical Sciences, UU
Funding: Centre for Interdisciplinary Mathematics, CIM, UU; TN-Faculty, UU
Period: 201706–
Abstract: We focus on improving the existing registration method for whole-body scans by including segmentation results as prior knowledge. Segmentation of the organs in the abdomen is a daunting task, as the organs vary a lot in their properties and size. And having a robust method to segment a number of them would not only be useful in clinical setting, but it could also help guide the registration method in those areas, which are most challenging to register. In search for such a method, we apply convolutional neural networks, look at various architectures, better sampling strategies and possibilities of including prior knowledge in the process. Preliminary results on improvements we achieved by integrating anatomical knowledge with a fully convolutional network (deepMedic) were presented at WiML in Long Beach.
Calving Detection
Robin Strand
Partner: Doroth´ee Vallot, Rickard Pettersson, Dept. of Earth Sciences, UU. Sigit Adinugroho, MSc student,
CBA; Penelope How, Institute of Geography, School of GeoSciences, University of Edinburgh, UK
Funding: TN-Faculty
Period: 20150101–
Abstract: Calving processes are an important unknown in glacier systems terminating in the ocean. Automatic image analyses methods for the analysis of calving fronts of glaciers are monitored by time-lapse cameras are developed in this project. The methods are based on detecting changes in segmented calving fronts of glaciers. The area of the calving event is then computed based on the relative camera position.
Mathematical and Geometrical Theory
Precise Image-Based Measurements through Irregular Sampling
Teo Asplund, Robin Strand, Gunilla Borgefors
Partner: Cris Luengo-Flagship Biosciences Inc., Westminster, Colorado, USA, Matthew Thurley-Luleå
University of Technology, Luleå
Funding: Swedish Research Council
Period: 20150401–
Abstract: We develop mathematical morphology on irregularly sampled signals. This is beneficial for a number of reasons 1. Irregularly sampled signals would traditionally have to be resampled onto the regular grid to allow morphology to be applied, however, such resampling can require interpolating data where the original signal contained large holes. This can lead to very poor performance. 2. The morphological operators depend on suprema/infima in the signal. A regularly sampled signal is likely to miss these. 3.
The operators produce lines along which the derivative is not continuous, thereby introducing unbounded frequencies and breaking the correspondence between the sampled signal and the continuous bandlimited one. 4. The structuring element is limited by the sampling grid. We have shown that moving to morphology on irregularly sampled signals can yield results that better approximate continuous morphology than the traditional morphological operators, yielding more accurate measurements both in 1D- and 2D grayscale morphology.
Digital Distance Functions and Distance Transforms
Robin Strand, Gunilla Borgefors
Partner: Benedek Nagy - Dept. of Computer Science, Faculty of Informatics, University of Debrecen, Hungary;
Nicolas Normand, IRCCyN - University of Nantes, France
Funding: TN-Faculty, UU
Period: 19930901–
Abstract: The distance between any two grid points in a grid is defined by a distance function. In this project, weighted distances have been considered for many years. A generalization of the weighted distances is obtained by using both weights and a neighborhood sequence to define the distance function.
The neighborhood sequence allows the size of the neighborhood to vary along the paths. A manuscript on distance functions based on multiple types of weighted steps combined with neighborhood sequences has been produced in collaboration with Strand, Nagy and Normand. The manuscript holds (mainly theoretical) results on for example metricity and optimal parameters. The figure below illustrates the shapes of disks with different number of weights, when the optimization criterion is roundness in the Euclidean sense.
The Minimum Barrier Distance
Robin Strand, Filip Malmberg
Partner: Punam K. Saha, Dept. of Electrical and Computer Engineering and Dept. of Radiology, University of Iowa, IA, USA; Krzysztof C. Ciesielski, Dept. of Mathematics, West Virginia University, Morgantown,
WV, USA; Dept. of Radiology, MIPG, University of Pennsylvania, PA, USA; Stan Sclaroff, Dept. of Computer
Science, Boston University, USA; Jianming Zhang, Adobe Research, San Jose, USA
Funding: TN-Faculty, UU
Period: 20110300–
Abstract: This project studies the minimum barrier distance (MBD), given by the difference between the maximum and minimum values that has to be passed to go from one point to another. Theoretical properties as well as efficient computational solutions for the MBD have been developed. During 2017, two papers related to the BMD were presented at the international conference on Discrete Geometry for Computer Imagery in Vienna. The first paper presented a summary and overview of previous research in this area. The second paper introduced a related distance function, the Boolean Map Distance (BMD). During the year, Filip Malmberg also visited Stan Sclaroff in Boston, to collaborate on further development of the BMD.
