Editorial for the MEDIA special issue on MICCAI 2012

Editorial of the Medical Image Analysis journal 17 (2013) 711International audienceno abstrac

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

Iowa Engineer, Volume 2014, No.3

https://ir.uiowa.edu/iowaengineer/1045/thumbnail.jp

Asclepios: a Research Project-Team at INRIA for the Analysis and Simulation of Biomedical Images

International audienceAsclepios1 is the name of a research project-team o cially launched on November 1st, 2005 at INRIA Sophia-Antipolis, to study the Analysis and Simulation of Biological and Medical Images. This research project-team follows a previous one, called Epidaure, initially dedicated to Medical Imaging and Robotics research. These two project teams were strongly supported by Gilles Kahn, who used to have regular scienti c in- teractions with their members. More generally, Gilles Kahn had a unique vision of the growing importance of the interaction of the Information Technologies and Sciences with the Biological and Medical world. He was one of the originators of the creation of a speci c BIO theme among the main INRIA research directions, which now regroups 16 di fferent research teams including Asclepios, whose research objectives are described and illustrated in this article

20th anniversary of the medical image analysis journal (MedIA)

International audienceThis special issue of Medical Image Analysis gathers 32 articles written by prominent members of the current editorial board of the journal. Each author had the opportunity to discuss a personal vision of the past and future of the field. The result is a set of outstanding visionary articles that will count as a historical landmark for our readership. It was shortly after the success of the first CVRMed conference 1 held in Nice in 1995 that we took the decision to launch a new journal entirely dedicated to the computational analysis of medical images. The founding editorial board members 2 of MedIA were among the authors of the articles of the first volume 3 of the journal published by Oxford University Press in 1996. Twenty years later, the journal (now published by Elsevier Science since 2000) has become a reference for a vibrant community of researchers working in academics, clinics and industry. It has also become the premier journal of the MICCAI Society, which organizes the Medical Image Computing and Computer Assisted Intervention conference each year on a different continent. Medical Image Analysis is now recognized as a flourishing research field at the intersection of Informatics, Computational Sciences and Medicine. Its progress contributes to the development of innovative computational tools to assist medical imaging professionals in clinical analysis and intervention

Fusion of interventional ultrasound & X-ray

In einer immer älter werdenden Bevölkerung wird die Behandlung von strukturellen Herzkrankheiten zunehmend wichtiger. Verbesserte medizinische Bildgebung und die Einführung neuer Kathetertechnologien führten dazu, dass immer mehr herkömmliche chirurgische Eingriffe am offenen Herzen durch minimal invasive Methoden abgelöst werden. Diese modernen Interventionen müssen durch verschiedenste Bildgebungsverfahren navigiert werden. Hierzu werden hauptsächlich Röntgenfluoroskopie und transösophageale Echokardiografie (TEE) eingesetzt. Röntgen bietet eine gute Visualisierung der eingeführten Katheter, was essentiell für eine gute Navigation ist. TEE hingegen bietet die Möglichkeit der Weichteilgewebedarstellung und kann damit vor allem zur Darstellung von anatomischen Strukturen, wie z.B. Herzklappen, genutzt werden. Beide Modalitäten erzeugen Bilder in Echtzeit und werden für die erfolgreiche Durchführung minimal invasiver Herzchirurgie zwingend benötigt. Üblicherweise sind beide Systeme eigenständig und nicht miteinander verbunden. Es ist anzunehmen, dass eine Bildfusion beider Welten einen großen Vorteil für die behandelnden Operateure erzeugen kann, vor allem eine verbesserte Kommunikation im Behandlungsteam. Ebenso können sich aus der Anwendung heraus neue chirurgische Worfklows ergeben. Eine direkte Fusion beider Systeme scheint nicht möglich, da die Bilddaten eine zu unterschiedliche Charakteristik aufweisen. Daher kommt in dieser Arbeit eine indirekte Registriermethode zum Einsatz. Die TEE-Sonde ist während der Intervention ständig im Fluoroskopiebild sichtbar. Dadurch wird es möglich, die Sonde im Röntgenbild zu registrieren und daraus die 3D Position abzuleiten. Der Zusammenhang zwischen Ultraschallbild und Ultraschallsonde wird durch eine Kalibrierung bestimmt. In dieser Arbeit wurde die Methode der 2D-3D Registrierung gewählt, um die TEE Sonde auf 2D Röntgenbildern zu erkennen. Es werden verschiedene Beiträge präsentiert, welche einen herkömmlichen 2D-3D Registrieralgorithmus verbessern. Nicht nur im Bereich der Ultraschall-Röntgen-Fusion, sondern auch im Hinblick auf allgemeine Registrierprobleme. Eine eingeführte Methode ist die der planaren Parameter. Diese verbessert die Robustheit und die Registriergeschwindigkeit, vor allem während der Registrierung eines Objekts aus zwei nicht-orthogonalen Richtungen. Ein weiterer Ansatz ist der Austausch der herkömmlichen Erzeugung von sogenannten digital reconstructed radiographs. Diese sind zwar ein integraler Bestandteil einer 2D-3D Registrierung aber gleichzeitig sehr zeitaufwendig zu berechnen. Es führt zu einem erheblichen Geschwindigkeitsgewinn die herkömmliche Methode durch schnelles Rendering von Dreiecksnetzen zu ersetzen. Ebenso wird gezeigt, dass eine Kombination von schnellen lernbasierten Detektionsalgorithmen und 2D-3D Registrierung die Genauigkeit und die Registrierreichweite verbessert. Zum Abschluss werden die ersten Ergebnisse eines klinischen Prototypen präsentiert, welcher die zuvor genannten Methoden verwendet.Today, in an elderly community, the treatment of structural heart disease will become more and more important. Constant improvements of medical imaging technologies and the introduction of new catheter devices caused the trend to replace conventional open heart surgery by minimal invasive interventions. These advanced interventions need to be guided by different medical imaging modalities. The two main imaging systems here are X-ray fluoroscopy and Transesophageal  Echocardiography (TEE). While X-ray provides a good visualization of inserted catheters, which is essential for catheter navigation, TEE can display soft tissues, especially anatomical structures like heart valves. Both modalities provide real-time imaging and are necessary to lead minimal invasive heart surgery to success. Usually, the two systems are detached and not connected. It is conceivable that a fusion of both worlds can create a strong benefit for the physicians. It can lead to a better communication within the clinical team and can probably enable new surgical workflows. Because of the completely different characteristics of the image data, a direct fusion seems to be impossible. Therefore, an indirect registration of Ultrasound and X-ray images is used. The TEE probe is usually visible in the X-ray image during the described minimal-invasive interventions. Thereby, it becomes possible to register the TEE probe in the fluoroscopic images and to establish its 3D position. The relationship of the Ultrasound image to the Ultrasound probe is known by calibration. To register the TEE probe on 2D X-ray images, a 2D-3D registration approach is chosen in this thesis. Several contributions are presented, which are improving the common 2D-3D registration algorithm for the task of Ultrasound and X-ray fusion, but also for general 2D-3D registration problems. One presented approach is the introduction of planar parameters that increase robustness and speed during the registration of an object on two non-orthogonal views. Another approach is to replace the conventional generation of digital reconstructedradiographs, which is an integral part of 2D-3D registration but also a performance bottleneck, with fast triangular mesh rendering. This will result in a significant performance speed-up. It is also shown that a combination of fast learning-based detection algorithms with 2D-3D registration will increase the accuracy and the capture range, instead of employing them solely to the  registration/detection of a TEE probe. Finally, a first clinical prototype is presented which employs the presented approaches and first clinical results are shown