CLEF 2017 working notes now available online

The CLEF 2017 Working Notes have been published in CEUR-WS as volume 1866: http://ceur-ws.org/Vol-1866/

The CLEF 2017 conference is the eighteenth edition of the popular CLEF campaign and workshop series which has run since 2000 contributing to the systematic evaluation of multilingual and multimodal information access systems, primarily through experimentation on shared tasks. In 2010 CLEF was launched in a new format, as a conference with research presentations, panels, poster and demo sessions and laboratory evaluation workshops. These are proposed and operated by groups of organizers volunteering their time and effort to define, promote, administrate and run an evaluation activity. CLEF 2017 was hosted by the ADAPT Centre , Dublin City University and Trinity College Dublin from the 11th to 14th September 2017. This year’s conference was also co-located with MediaEval and the program included joint sessions between both MediaEval and CLEF to allow for cross fertilisation.

Medical Computer Vision 2016 proceedings available online

The proceedings from the Medical Computer Vision 2016 workshop, from MICCAI2016 are now available online.

The goal of the MCV workshop is to explore the use of “big data” algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images.
The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis.

NVIDIA GPU grant approved for MEGANE PRO project

The NVIDIA GPU grant program awarded a Titan Xp to the MEGANE PRO project.

Hand amputations are highly impairing and can dramatically affect the capabilities people. Man-machine interfaces that can control hand prostheses have been developed, but natural control methods are still only rarely applied in real life. The requested GPU will allow us to develop this research field that has high scientific and social impact. The project includes the development of highly specific data classification and fusion algorithms based on convolutional and recurrent neural networks. The algorithms will allow to control a 3D printed prosthetic hand and/or a robotic hand simulator based on virtual reality in real time.

ICORR2017 paper selected for best poster competition

The ICORR 2017 submission ‘Megane Pro: myo-electricity, visual and gaze tracking data acquisitions to improve hand prostethics’ by Francesca Giordaniello et al. was selected for the Rehabweek Best poster completion.

The IEEE International Conference on Rehabilitation Robotics (ICORR) 2017 will take place from July 17-20 in London, UK.

Abstract

During the past 60 years scientific research proposed many techniques to the control of robotic hand prostheses with surface electromyography (sEMG). Few of them have been implemented in commercial systems also due to robustness that may be improved with multimodal data. In this paper we introduce the first acquisition setup, acquisition protocol and dataset including surface electromyography (sEMG), eye tracking and computer vision to study robotic hand control. A data analysis on data from healthy controls gives a first ideas of the capabilities and constraints of the acquisition procedure that is will be applied to amputees in a next step. Different data sources are not fused together in the analysis. Nevertheless, the results support the use of the proposed multimodal data acquisition approach for prosthesis control. The sEMG movement classification results confirm that it is possible to classify several grasps with sEMG alone. sEMG can detect the grasp type and also small differences in the grasped object (accuracy: 95%). The simultaneous recording of eye tracking and scene camera data shows that these sensors allow performing object detection for grasp selection and that several neurocognitive parameters need to be taken into account for this. In conclusion, this work on intact subjects presents an innovative acquisition setup and protocol. The first results in terms of data analysis are promising and set the basis for future work on amputees, aiming to improve the robustness of prostheses with multimodal data.

VISCERAL book now available online

The book ‘Cloud-based benchmarking of medical image analysis’  related to the VISCERAL project can now be accessed online

This book presents the VISCERAL project benchmarks for analysis and retrieval of 3D medical images (CT and MRI) on a large scale, which used an innovative cloud-based evaluation approach where the image data were stored centrally on a cloud infrastructure and participants placed their programs in virtual machines on the cloud. The book presents the points of view of both the organizers of the VISCERAL benchmarks and the participants.

The book is divided into five parts. Part I presents the cloud-based benchmarking and Evaluation-as-a-Service paradigm that the VISCERAL benchmarks used. Part II focuses on the datasets of medical images annotated with ground truth created in VISCERAL that continue to be available for research. It also covers the practical aspects of obtaining permission to use medical data and manually annotating 3D medical images efficiently and effectively. The VISCERAL benchmarks are described in Part III, including a presentation and analysis of metrics used in evaluation of medical image analysis and search. Lastly, Parts IV and V present reports by some of the participants in the VISCERAL benchmarks, with Part IV devoted to the anatomy benchmarks and Part V to the retrieval benchmark.

This book has two main audiences: the datasets as well as the segmentation and retrieval results are of most interest to medical imaging researchers, while eScience and computational science experts benefit from the insights into using the Evaluation-as-a-Service paradigm for evaluation and benchmarking on huge amounts of data.

Article on 3D solid texture classification now available in IEEEXplore

The journal article ‘3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms’ is now available under the ‘Early Access’ area in IEEEXplore. 

The article will appear on the April issue of the IEEE Transactions on Image Processing journal. 

Abstract:

Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3–D solid images. Computational analysis of these images is complex but necessary since it is difficult for humans to visualize and quantify their detailed 3–D content. One of the most common methods to analyze 3–D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3–D. Current state–of–the– art techniques face many challenges when working with 3–D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3–D Riesz– wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher–order 3–D Riesz–wavelet transforms. The estimations of local texture orientations are based on higher– order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3–D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which is better than all other techniques that are published and tested on the same database.

Chapter on Big data analysis in Forensics book now published

The Chapter ‘Big data in medical imaging, forensics and beyond’ by Henning Müller has now been published in the book Forensigraphie – Möglichkeiten und Grenzen IT-gestützter klinisch-forensischer Bildgebung
Info

Möglichkeiten und Grenzen IT-gestützter klinisch-forensischer bildgebender Verfahren werden in diesem interdisziplinären Band aus juristischer, rechtsmedizinischer und technischer Sicht analysiert.

Paper on 3D texture analysis accepted for IEEE TIP

The paper ‘3-D Solid Texture Classification Using Locally-Oriented Wavelet Transforms’ by Dicente et al. has been accepted for publication at IEEE Transactions on Image Processing.

Abstract
Many image acquisition techniques used in biomedical imaging, material analysis, or structural geology are capable to acquire 3–D solid images. Computational analysis of these images is complex but necessary, since it is difficult for humans to visualize and quantify their detailed 3–D content. One of the most common methods to analyze 3–D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3–D. Current state–of–the–art techniques face many challenges when working with 3–D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3–D Riesz–wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and to compare textures with arbitrary (local) orientations. This paper compares three local alignment criteria for higher–order 3–D Riesz–wavelet transforms. The estimations of local texture orientations are based on higher–order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3–D solid textures with alterations (e.g., rotations, noise) demonstrated the importance of local directional information for robust and
accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors by up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which
are better than the other techniques tested on the same database.

Abstract

Many image acquisition techniques used in biomedical imaging, material analysis, or structural geology are capable to acquire 3–D solid images. Computational analysis of these images is complex but necessary, since it is difficult for humans to visualize and quantify their detailed 3–D content. One of the most common methods to analyze 3–D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3–D. Current state–of–the–art techniques face many challenges when working with 3–D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3–D Riesz–wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and to compare textures with arbitrary (local) orientations. This paper compares three local alignment criteria for higher–order 3–D Riesz–wavelet transforms. The estimations of local texture orientations are based on higher–order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3–D solid textures with alterations (e.g., rotations, noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors by up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which are better than the other techniques tested on the same database. 

Lung database paper selected as candidate for the Robert F. Wagner Best Student paper Award

The paper ‘Making Sense of Large Data Sets without Annotations:Analyzing Age-related Correlations from Lung CT Scans’ by Yashin Dicente et al. is one of the finalists for the Robert F. Wagner Best Student paper Award at SPIE2017. The conference will take place from the 11-16 February 2017 in Orlando, Florida, US.

Abstract:
 The analysis of large data sets can help to gain knowledge about specic organs or on specic diseases, just as big data analysis does in many non-medical areas. This article aims to gain information from 3D volumes, so the visual content of lung CT scans of a large number of patients. In the case of the described data set, only little annotation is available on the patients that were all part of an ongoing screening program and besides age and gender no information on the patient and the ndings was available for this work. This is a scenario that can happen regularly as image data sets are produced and become available in increasingly large quantities but manual annotations are often not available and also clinical data such as text reports are often harder to share. We extracted a set of visual features from 12,414 CT scans of 9,348 patients that had CT scans of the lung taken in the context of a national lung screening program in Belarus. Lung elds were segmented by two segmentation algorithms and only cases where both algorithms were able to nd left and right lung and had a Dice coecient above 0.95 were analyzed. This assures that only segmentations of good quality were used to extract features of the lung. Patients ranged in age from 0 to 106 years. Data analysis shows that age can be predicted with a fairly high accuracy for persons under 15 years. Relatively good results were also obtained between 30 and 65
years where a steady trend is seen. For young adults and older people the results are not as good as variability is very high in these groups. Several visualizations of the data show the evolution patters of the lung texture, size and density with age. The experiments allow learning the evolution of the lung and the gained results show that even with limited meta{data we can extract interesting information from large{scale visual data. These age-related changes (for example of the lung volume, the density histogram of the tissue) can also be taken into account for the interpretation of new cases. The database used includes patients that had suspicions on a chest X-ray, so it is not a group of healthy people, and only tendencies and not a model of a healthy lung at a specic age can be derived.

MedGIFT group added as a member of COST actions 2016

Prof. Henning Müller is part of the Management Committe of the approved European Cooperation in Science and Technology (COST) action European network for translational research in children’s and adult interstitial lung disease

COST Actions are a flexible, fast, effective and efficient networking instrument for researchers, engineers and scholars to cooperate and coordinate nationally funded research activities. COST Actions allow European researchers to jointly develop their own ideas in any science and technology field.

Description

Interstitial lung disease in children (chILD) is a term that describes a collection of more than 200 rare lung disorders. It is a heterogeneous group of non-neoplastic disorders resulting from damage by varying patterns of inflammation and fibrosis with the interstitium as the primary site of injury. As with other orphan diseases, chILD data is lacking on the natural course, phenotypic variability, associations with genotype, and effectiveness of treatments. The disease course is very variable, and depending on more than just the underlying cause; for example, within a given family, the phenotypic variability of chILD such as surfactant protein C mutation is huge. The rarity of individual chILDs contributes to a lack of randomised control trial data on effectiveness of treatments. Management strategies derive from other diseases or are based on physicians experience and remain controversial.

This Action will create a pan-Europe-led network of multidisciplinary clinicians (adult and paediatric), scientists, and patients and their families with the aim of accurate and early diagnosis with structured, potentially personalised, management and therapies. The Action will stimulate and coordinate multidisciplinary research in chILD from infancy to adulthood, as well as reveal the pathophysiological commonalities between different forms ILD at the molecular level. The results of these efforts will create large incremental changes in understanding and management of chILD. Since chILD is an umbrella term for a number of conditions most of which imply more than purely medical or scientific expertise, the Action will pay due attention to the larger societal implications of chILD research.

Interstitial lung disease in children (chILD) is a term that describes a collection of more than 200 rare lung disorders. It is a heterogeneous group of non-neoplastic disorders resulting from damage by varying patterns of inflammation and fibrosis with the interstitium as the primary site of injury. As with other orphan diseases, chILD data is lacking on the natural course, phenotypic variability, associations with genotype, and effectiveness of treatments. The disease course is very variable, and depending on more than just the underlying cause; for example, within a given family, the phenotypic variability of chILD such as surfactant protein C mutation is huge. The rarity of individual chILDs contributes to a lack of randomised control trial data on effectiveness of treatments. Management strategies derive from other diseases or are based on physicians experience and remain controversial.

This Action will create a pan-Europe-led network of multidisciplinary clinicians (adult and paediatric), scientists, and patients and their families with the aim of accurate and early diagnosis with structured, potentially personalised, management and therapies. The Action will stimulate and coordinate multidisciplinary research in chILD from infancy to adulthood, as well as reveal the pathophysiological commonalities between different forms ILD at the molecular level. The results of these efforts will create large incremental changes in understanding and management of chILD. Since chILD is an umbrella term for a number of conditions most of which imply more than purely medical or scientific expertise, the Action will pay due attention to the larger societal implications of chILD research