Figure 42: Word-cloud of abstracts of all reviewed conference proceedings that were published in 2019 6.1 Edited conference proceedings
1. Proceedings from 21st Scandinavian Conference on Image Analysis Book: Lecture Notes in Computer Science, Vol. 11482
Editors: Michael Felsberg, Per-Erik Forss´en,Ida-Maria Sintorn, Jonas Unger
Publisher: Springer Abstract: This volume constitutes the refereed proceedings of the 21st Scandinavian Conference on Image Analysis, SCIA 2019, held in Norrk¨oping, Sweden, in June 2019.
The 40 revised papers presented were carefully reviewed and selected from 63 submissions. The contribu-tions are structured in topical seccontribu-tions on Deep convolutional neural networks; Feature extraction and image analysis; Matching, tracking and geometry; and Medical and biomedical image analysis.
2. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications Book: Lecture Notes in Computer Science, Vol. 11896
Editors:Ingela Nystr¨om, Yanio Hernandez Heredia(1), Vladimir Mili´an N`u˜nez(1) (1) University of Informatics Sciences, Havana, Cuba
Publisher: Springer Verlag Abstract: This volume constitutes the refereed proceedings of the 24th Iberoamer-ican Congress on Pattern Recognition (CIARP 2019), held in Havana, Cuba, in October 2019.
and real multichannel images confirms good performance, particularly relevant when subpixel precision in segmentation and subsequent analysis is a requirement.
2. Deep Learning in Image Cytometry: A Review
Authors: Anindya Gupta, Philip J. Harrison(1), H˚akan Wieslander, Nicolas Pielawski, Kimmo Kar-tasalo(2,3), Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm(4), Ola Spjuth(1), Ida-Maria Sintorn, Carolina W¨ahlby(4)
(1) Dept. of Pharmaceutical Biosciences, UU
(2) Faculty of Medicine and Life Sciences, University of Tampere, Finland
(3) Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Finland (4) BioImage Informatics Facility, SciLifeLab, UU
Journal: Cytometry, Vol. 95, No. 4, pp. 366–380 DOI: 10.1002/cyto.a.23701
Abstract: Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Start-ing with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data.
3. RayCaching: Amortized Isosurface Rendering for Virtual Reality Authors:Fredrik Nysj¨o, Filip Malmberg, Ingela Nystr¨om
Journal: Computer Graphics Forum, Vol. 39, No. 1, pp. 220–230 DOI: 10.1111/cgf.13762
Abstract: Real-time virtual reality requires efficient rendering methods to deal with high- resolution stereo-scopic displays and low latency head-tracking. Our proposed RayCaching method renders isosurfaces of large volume datasets by amortizing raycasting over several frames and caching primary rays as small bricks that can be efficiently rasterized. An occupancy map in form of a clipmap provides level of detail and en-sures that only bricks corresponding to visible points on the isosurface are being cached and rendered. Hard shadows and ambient occlusion from secondary rays are also accumulated and stored in the cache. Our method supports real-time isosurface rendering with dynamic isovalue and allows stereoscopic visualiza-tion and exploravisualiza-tion of large volume datasets at framerates suitable for virtual reality applicavisualiza-tions.
4. Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous
Authors: Jeremy Adler(1),Ida-Maria Sintorn, Robin Strand, Ingela Parmryd(1,2) (1) SciLifeLab, Medical Cell Biology, UU
(2) Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Sweden Journal: Communications Biology, Vol. 2, Article number: 12
DOI: 10.1038/s42003-018-0240-2
Abstract: Cells are neither flat nor smooth, which has serious implications for prevailing plasma membrane models and cellular processes like cell signalling, adhesion and molecular clustering. Using probability distributions from diffusion simulations, we demonstrate that 2D and 3D Euclidean distance measurements substantially underestimate diffusion on non-flat surfaces. Intuitively, the shortest within surface distance (SWSD), the geodesic distance, should reduce this problem. The SWSD is accurate for foldable surfaces but, although it outperforms 2D and 3D Euclidean measurements, it still underestimates movement on de-formed surfaces. We demonstrate that the reason behind the underestimation is that topographical features themselves can produce both super- and subdiffusion, i.e. the appearance of anomalous diffusion. Differen-tiating between topography-induced and genuine anomalous diffusion requires characterising the surface by simulating Brownian motion on high-resolution cell surface images and a comparison with the experimental data.
5. Developing adaptive traffic signal control by actor–critic and direct exploration methods Authors: Mohammad Aslani(1), Mohammad Saadi Mesgari(2),Stefan Seipel(1), Marco Wiering(3)
(1) Dept. of Industrial Development, IT and Land Management, University of G¨avle, Sweden
(2) Dept. of Geospatial Information System (GIS), Faculty of Geodesy and Geomatics Engineering, K.N.
Toosi University of Technology, Tehran, Iran
(3) Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen, the Netherlands Journal: Proceedings of the Institution of Civil Engineers - Transport, Vol. 172 Issue 5, pp. 289–298 DOI: 10.1680/jtran.17.00085
Abstract: Designing efficient traffic signal controllers has always been an important concern in traffic engi-neering. This is owing to the complex and uncertain nature of traffic environments. Within such a context, reinforcement learning has been one of the most successful methods owing to its adaptability and its online learning ability. Reinforcement learning provides traffic signals with the ability automatically to determine the ideal behaviour for achieving their objective (alleviating traffic congestion). In fact, traffic signals based on reinforcement learning are able to learn and react flexibly to different traffic situations without the need of a predefined model of the environment. In this research, the actor–critic method is used for adaptive traf-fic signal control (ATSC-AC). Actor–critic has the advantages of both actor-only and critic-only methods.
One of the most important issues in reinforcement learning is the trade-off between exploration of the traf-fic environment and exploitation of the knowledge already obtained. In order to tackle this challenge, two direct exploration methods are adapted to traffic signal control and compared with two indirect exploration methods. The results reveal that ATSC-ACs based on direct exploration methods have the best performance and they consistently outperform a fixed-time controller, reducing average travel time by 21%.
6. Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology
Authors: Christophe Avenel(1), Anna Tolf(2), Anca Dragomir(2,3),Ingrid B. Carlbom(1) (1) CADESS Medical AB, Uppsala, Sweden
(2) Dept. of Pathology, Uppsala University Hospital (3) Dept. of Immunology, Genetics and Pathology, UU
Journal: Frontiers in Bioengineering and Biotechnology, Vol. 7, article-id 125 DOI: 10.3389/fbioe.2019.00125
Abstract: Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists’ workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisi-tion to keep color and intensity variaacquisi-tions to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions.
Since malignancy grading of prostate tissue according to Gleason or to the International Society of Uro-logical Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differ-entiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that can successfully determine the glandular boundaries. Accu-rate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands.
7. PDNet: Semantic segmentation integrated with a primal-dual network for document binarization Authors:Kalyan Ram Ayyalasomayajula, Filip Malmberg, Anders Brun
Journal: Pattern Recognition Letters, Vol. 121, pp. 52–60 DOI: 10.1016/j.patrec.2018.05.011
Abstract: Binarization of digital documents is the task of classifying each pixel in an image of the docu-ment as belonging to the background (parchdocu-ment/paper) or foreground (text/ink). Historical docudocu-ments are often subjected to degradations, that make the task challenging. In the current work a deep neural network architecture is proposed that combines a fully convolutional network with an unrolled primal-dual network that can be trained end-to-end to achieve state of the art binarization on four out of seven datasets.
Docu-ment binarization is formulated as an energy minimization problem. A fully convolutional neural network is trained for semantic segmentation of pixels that provides labeling cost associated with each pixel. This cost estimate is refined along the edges to compensate for any over or under estimation of the foreground class using a primal-dual approach. We provide necessary overview on proximal operator that facilitates theoretical underpinning required to train a primal-dual network using a gradient descent algorithm. Numer-ical instabilities encountered due to the recurrent nature of primal-dual approach are handled. We provide experimental results on document binarization competition dataset along with network changes and hyper-parameter tuning required for stability and performance of the network. The network when pre-trained on synthetic dataset performs better as per the competition metrics.
8. TAF1, associated with intellectual disability in humans, is essential for embryogenesis and regulates neurodevelopmental processes in zebrafish
Authors: Sanna Gudmundsson(1), Maria Wilbe(1), Beata Filipek-G´orniok(2), Anna-Maja Molin(1), Sara Ekvall(1), Josefin Johansson(1), Amin Allalou, Hans Gylje(3), Vera M. Kalscheuer(4), Johan Ledin(2), G¨oran Anner´en(1), Marie-Louise Bondeson(1)
(1) Dept. of Immunology, Genetics and Pathology and SciLifeLab, UU
(2) Dept. of Organismal Biology, Genome Engineering Zebrafish and SciLifeLab, UU (3) Dept. of Paediatrics, Central Hospital, V¨aster˚as
(4) Research Group Development and Disease, Max Planck Institute for Molecular Genetics, Berlin, Ger-many
Journal: Scientific Reports, Vol. 9, Article number: 10730 DOI: 10.1038/s41598-019-46632-8
Abstract: The TATA-box binding protein associated factor 1 (TAF1) protein is a key unit of the transcrip-tion factor II D complex that serves a vital functranscrip-tion during transcriptranscrip-tion initiatranscrip-tion. Variants of TAF1 have been associated with neurodevelopmental disorders, but TAF1’s molecular functions remain elusive. In this study, we present a five-generation family affected with X-linked intellectual disability that co-segregated with a TAF1 c.3568C>T, p.(Arg1190Cys) variant. All affected males presented with intellectual disabil-ity and dysmorphic features, while heterozygous females were asymptomatic and had completely skewed X-chromosome inactivation. We investigated the role of TAF1 and its association to neurodevelopment by creating the first complete knockout model of the TAF1 orthologue in zebrafish. A crucial function of hu-man TAF1 during embryogenesis can be inferred from the model, demonstrating that intact taf1 is essential for embryonic development. Transcriptome analysis of taf1 zebrafish knockout revealed enrichment for genes associated with neurodevelopmental processes. In conclusion, we propose that functional TAF1 is essential for embryonic development and specifically neurodevelopmental processes.
9. Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks
Authors:Anindya Gupta, Tonis Saar(1), Olev Martens(2), Yannick Le Moullec(2), Ida-Maria Sintorn (1) Eliko Tehnoloogia Arenduskeskus O ¨U, Tallinn, Estonia
(2) Thomas Johann Seebeck Dept. of Electronics, Tallinn University of Technology, Estonia Journal: International journal of imaging systems and technology, 13 pages
DOI: 10.1002/ima.22373
Abstract: Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error-prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer-assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidate-screening module and a false positive (FP) reduction module. The candidate-screening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features-based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro-nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well-suited FP
reduction module of a CAD scheme.
10. Impact of Q-Griffithsin anti-HIV microbicide gel in non-human primates: In situ analyses of epithe-lial and immune cell markers in rectal mucosa
Authors: G¨okc¸e G¨unaydın(1), Gabriella Edfeldt(1), David A. Garber(2), Muhammad Asghar(1), Laura No˜el-Romas(3), Adam Burgener(1,3),Carolina W¨ahlby, Lin Wang(4), Lisa C. Rohan(4,5), Patricia Guen-thner(2), James Mitchell(2), Nobuyuki Matoba(6,7,8), Janet M. McNicholl(2), Kenneth E. Palmer(6,7,8), Annelie Tjernlund(1), Kristina Broliden(1)
(1) Dept. of Medicine Solna, Division of Infectious Diseases, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
(2) Laboratory Branch, Division of HIV/AIDS Prevention, National Centre for HIV/AIDS, Viral Hepatitis, Sexually Transmitted Disease and Tuberculosis Prevention, CDC, Atlanta, USA
(3) National HIV and Retrovirology Labs, JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada, and Dept. of Obstetrics & Gynecology, and Dept. of Medical Mi-crobiology, University of Manitoba, Canada
(4) Magee-Womens Research Institute, Pittsburgh, USA (5) School of Pharmacy, University of Pittsburgh, USA
(6) Center for Predictive Medicine, University of Louisville, USA (7) Dept. of Pharmacology and Toxicology, University of Louisville, USA (8) James Graham Brown Cancer Center, University of Louisville, USA Journal: Scientific Reports volume 9, Article number: 18120
DOI: 0.1038/s41598-019-54493-4
Abstract: Natural-product derived lectins can function as potent viral inhibitors with minimal toxicity as shown in vitro and in small animal models. We here assessed the effect of rectal application of an anti-HIV lectin-based microbicide Q-Griffithsin (Q-GRFT) in rectal tissue samples from rhesus macaques. E-cadherin+ cells, CD4+ cells and total mucosal cells were assessed using in situ staining combined with a novel customized digital image analysis platform. Variations in cell numbers between baseline, placebo and Q-GRFT treated samples were analyzed using random intercept linear mixed effect models. The frequencies of rectal E-cadherin+ cells remained stable despite multiple tissue samplings and Q-GRFT gel (0.1%, 0.3%
and 1%, respectively) treatment. Whereas single dose application of Q-GRFT did not affect the frequen-cies of rectal CD4+ cells, multi-dose Q-GRFT caused a small, but significant increase of the frequenfrequen-cies of intra-epithelial CD4+ cells (placebo: median 4%; 1% Q-GRFT: median 7%) and of the CD4+ lamina propria cells (placebo: median 30%; 0.1–1% Q-GRFT: median 36–39%). The resting time between sam-pling points were further associated with minor changes in the total and CD4+ rectal mucosal cell levels.
The results add to general knowledge of in vivo evaluation of anti-HIV microbicide application concerning cellular effects in rectal mucosa.
11. Generalized convexity: The case of lineally convex Hartogs domains Author:Christer O. Kiselman
Journal: Annales Polonici Mathematici, Vol. 123, pp. 319–344 DOI: 10.4064/ap180930-3-4
Abstract: Inspired by mathematical morphology we study generalized convexity and prove that certain subsets of Hartogs domains are convex in a generalized sense.
12. Language choice in scientific writing: The case of mathematics at Uppsala University and a Nordic journal
Author:Christer O. Kiselman
Journal: Nordisk Matematisk Tidskrift, Vol. 61, No. 2–4, pp. 111–132
Abstract: For several centuries following its founding in 1477, all lectures and dissertations at Uppsala University were in Latin. Then, during one century, from 1852 to 1953, several languages came into use:
there was a transition from Latin to Swedish, and then to French, German, and English. In the paper this transition is illustrated by language choice in doctoral theses in mathematics and in a Nordic journal.
13. Werner Fenchel, a pioneer in convexity theory and a migrant scientist Author:Christer O. Kiselman
Journal: Nordisk Matematisk Tidskrift, Vol. 61, No. 2–4, pp. 133–152
Abstract: Werner Fenchel was a pioneer in introducing duality in convexity theory. He got his PhD in Berlin, was forced to leave Germany, moved to Denmark, and lived during almost two years in Sweden. In
a private letter he explained his views on the development of duality in convexity theory and the many terms that have been used to describe it. The background for his move from Germany is sketched.
14. Tracking Microscope Performance: A Workflow to Compare Point Spread Function Evaluations Over Time
Authors:Anna H. Klemm(1,2), Andreas W. Thomae(2), Katarina Wachal(2) and Steffen Dietzel(2) (1) Bioimage Informatics Facility, SciLifeLab, UU
(2) Core Facility Bioimaging at the Biomedical Center and Walter-Brendel-Zentrum f¨ur Experimentelle Medizin, Ludwig-Maximilians-Univerist¨at M¨unchen, Germany
Journal: Microscopy and Microanalysis, Vol. 25, Issue 3, pp. 699–704 DOI: 10.1017/S1431927619000060
Abstract: Routine system checks are essential for supervising the performance of an advanced light micro-scope. Recording and evaluating the point spread function (PSF) of a given system provides information about the resolution and imaging. We compared the performance of fluorescent and gold beads for PSF recordings. We then combined the open-source evaluation software PSFj with a newly developed KNIME pipeline named PSFtracker to create a standardized workflow to track a system’s performance over several measurements and thus over long time periods. PSFtracker produces example images of recorded PSFs, plots full-width-half-maximum (FWHM) measurements over time and creates an html file which embeds the images and plots, together with a table of results. Changes of the PSF over time are thus easily spot-ted, either in FWHM plots or in the time series of bead images which allows recognition of aberrations in the shape of the PSF. The html file, viewed in a local browser or uploaded on the web, therefore provides intuitive visualization of the state of the PSF over time. In addition, uploading of the html file on the web allows other microscopists to compare such data with their own.
15. Modeling Spatial Correlation of Transcripts with Application to Developing Pancreas
Authors: Ruishan Liu(1), Marco Mignardi(2,5), Robert Jones(2), Martin Enge(2), Seung K. Kim(3), Stephen R. Quake(2,5), James Zou(4,5)
(1) Dept. of Electrical Engineering, Stanford University, USA
(2) Dept. of Bioengineering and Applied Physics, Stanford University, USA (3) Dept. of Developmental Biology, Stanford University, USA
(4) Dept. of Biomedical Data Science, Stanford University, USA (5) Chan-Zuckerberg Biohub, San Francisco, USA
Journal: Scientific Reports, Vol. 9, Article number: 5592 DOI: 10.1038/s41598-019-41951-2
Abstract: Recently high-throughput image-based transcriptomic methods were developed and enabled re-searchers to spatially resolve gene expression variation at the molecular level for the first time. In this work, we develop a general analysis tool to quantitatively study the spatial correlations of gene expression in fixed tissue sections. As an illustration, we analyze the spatial distribution of single mRNA molecules measured by in situ sequencing on human fetal pancreas at three developmental time points–80, 87 and 117 days post-fertilization. We develop a density profile-based method to capture the spatial relationship between gene expression and other morphological features of the tissue sample such as position of nuclei and endocrine cells of the pancreas. In addition, we build a statistical model to characterize correlations in the spatial distribution of the expression level among different genes. This model enables us to infer the inhibitory and clustering effects throughout different time points. Our analysis framework is applicable to a wide variety of spatially-resolved transcriptomic data to derive biological insights.
16. Relationship Between Endothelium-dependent Vasodilation and Fat Distribution using the new ”Imiomics”
Image Analysis Technique
Authors: Lars Lind(1),Robin Strand(3), Karl Michaelsson(2), Joel Kullberg(3,4), H˚akan Ahlstr¨om(3,4) (1) Dept. of Medical Sciences, UU
(2) Dept. of Surgical Sciences, UU
(3) Division of Radiology, Dept. of Surgical Sciences, UU (4) Antaros Medical, BioVenture Hub, M¨olndal, Sweden
Journal: Nutrition, Metabolism & Cardiovascular Diseases, Vol. 29(10), pp. 1077–1086 DOI: 10.1016/j.numecd.2019.06.017
Abstract:
BACKGROUND AND AIMS: We investigated how vasoreactivity in the brachial artery and the forearm resistance vessels were related to fat distribution and tissue volume, using both traditional imaging analysis
and a new technique, called ”Imiomics”, whereby vasoreactivity was related to each of the >2M 3D image elements included in the whole-body magnetic resonance imaging (MRI).
METHODS AND RESULTS: In 326 subjects in the Prospective investigation of Obesity, Energy and Metabolism (POEM) study (all aged 50 years), endothelium-dependent vasodilation was measured by acetylcholine infusion in the brachial artery (EDV) and flow-mediated vasodilation (FMD). Fat distribu-tion was evaluated by dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI).
EDV, but not FMD, was significantly related to total fat mass, liver fat, subcutaneous (SAT) and visceral (VAT) adipose tissue in a negative fashion in women, but not in men. Using Imiomics, an inverse relation-ship was seen between EDV and a local tissue volume of SAT in both the upper part of the body, as well as the gluteo-femoral part and the medial parts of the legs in women. Also the size of the liver, heart and VAT was inversely related to EDV. In men, less pronounced relationships were seen. FMD was also significantly related to local tissue volume of upper-body SAT and liver fat in women, but less so in men.
CONCLUSION: EDV, and to a lesser degree also FMD, were related to liver fat, SAT and VAT in women, but less so in men. Imiomics both confirmed findings from traditional methods and resulted in new, more detailed results.
17. Proof of principle study of a detailed whole-body image analysis technique, ”Imiomics”, regarding adipose and lean tissue distribution
Authors: Lars Lind(1), Joel Kullberg(2,3), H˚akan Ahlstr¨om(2,3), Karl Michaelsson(4),Robin Strand(2) (1) Dept. of Medical Sciences, UU
(2) Division of Radiology, Dept. of Surgical Sciences, UU (3) Antaros Medical, BioVenture Hub, M¨olndal, Sweden (4) Dept. of Surgical Sciences, UU
Journal: Scientific Reports, Vol. 9, Article number 7388 DOI: 10.1038/s41598-019-43690-w
Abstract: This ”proof-of-principle” study evaluates if the recently presented ”Imiomics” technique could visualize how fat and lean tissue mass are associated with local tissue volume and fat content at high/
unprecedented resolution. A whole-body quantitative water-fat MRI scan was performed in 159 men and 167 women aged 50 in the population-based POEM study. Total fat and lean mass were measured by DXA.
Fat content was measured by the water-fat MRI. Fat mass and distribution measures were associated to the detailed differences in tissue volume and fat concentration throughout the body using Imiomics. Fat mass was positively correlated (r > 0.50, p < 0.05) with tissue volume in all subcutaneous areas of the body, as well as volumes of the liver, intraperitoneal fat, retroperitoneal fat and perirenal fat, but negatively to lung volume. Fat mass correlated positively with volumes of paravertebral muscles, and muscles in the ventral part of the thigh and lower limb. Fat mass was distinctly correlated with the fat content in subcutaneous adipose tissue at the trunk. Lean mass was positively related to the large skeletal muscles and the skeleton. The present study indicates the Imiomics technique to be suitable for studies of fat and lean tissue distribution, and feasible for large scale studies.
18. A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities Authors: Marie-Ange Lebre(1), Antoine Vacavant(1), Manuel Grand-Brochier(1), Hugo Rositi(1), Robin Strand, Hubert Rosier (2), Armand Abergel (1), Pascal Chabrot (1), Benoit Magnin (1)
(1) Universit´e Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
(2) Centre Hospitalier ´Emile Roux, Le Puy-en-Velay, France
Journal: Computerized Medical Imaging and Graphics, Vol. 76, Article number 101635 DOI: 10.1016/j.compmedimag.2019.05.003
Abstract: Developing methods to segment the liver in medical images, study and analyze it remains a sig-nificant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambigu-ous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3% on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from