Dr. Adrien Depeursinge Profile

Dr. Adrien Depeursinge

at HES-SO Valais-Wallis

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Author

Publications (17)

Proceedings Article | 10 March 2020 Presentation + Paper

An exploration of uncertainty information for segmentation quality assessment

Katharina Hoebel, Vincent Andrearczyk, Andrew Beers, Jay Patel, Ken Chang, Adrien Depeursinge, Henning Müller, Jayashree Kalpathy-Cramer

Proceedings Volume 11313, 113131K (2020) https://doi.org/10.1117/12.2548722

KEYWORDS: Image segmentation, Data modeling, Lung, Statistical modeling, Medical imaging, Visualization

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SPIE Journal Paper | 6 June 2019

Neural network training for cross-protocol radiomic feature standardization in computed tomography

Vincent Andrearczyk, Adrien Depeursinge, Henning Müller

JMI, Vol. 6, Issue 02, 024008, (June 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.024008

KEYWORDS: Scanners, Feature extraction, Principal component analysis, Neural networks, Computed tomography, Image classification, Medical imaging, Reconstruction algorithms, Tissues, Image acquisition

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Proceedings Article | 15 March 2019 Presentation + Paper

Learning cross-protocol radiomics and deep feature standardization from CT images of texture phantoms

Vincent Andrearczyk, Adrien Depeursinge, Henning Müller

Proceedings Volume 10954, 109540I (2019) https://doi.org/10.1117/12.2512683

KEYWORDS: Scanners, Feature extraction, Principal component analysis, Computed tomography, Neural networks, Tissues, Medical imaging, Lung cancer, Image processing, Image acquisition

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Proceedings Article | 13 March 2019 Paper

A lung graph model for the classification of interstitial lung diseases on CT images

Guillaume Vanoost, Yashin Dicente Cid, Daniel Rubin, Adrien Depeursinge

Proceedings Volume 10950, 109503H (2019) https://doi.org/10.1117/12.2512866

KEYWORDS: Lung, Computed tomography, Image classification, Visualization, Tissues, Medicine

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Diagnosing Interstitial Lung Diseases (ILD) is a difficult task. It requires experienced chest radiologists that may not be available in less-specialized health centers. Moreover, a correct diagnosis is needed to decide for an appropriate treatment and prognostic. In this paper, we focus on the classification of 3 common subtypes of ILDs: Usual Interstitial Pneumonia (UIP), Non-Specific Interstitial Pneumonia (NSIP) and Chronic Hypersensitivity Pneumonitis (CHP). We propose a graph model of the lungs built from a large dataset. The structure of the graph is inspired from medical knowledge of disease predominance, where the nodes correspond to 24 distinct regions obtained from lateral, anterior-posterior and vertical splits of the images. The adjacency matrix is built from distances between intensity distributions of distinct regions. Graphs models are interpretable and were successfully used in neuroimaging. However, to the best of our knowledge, this is the first attempt to use a graph model of the lungs for classifying ILDs. In the particular case of ILDs, graph methods are relevant for the following reasons. In order to differentiate between the subtypes, not only the types of local patterns of the disease are important but also their anatomical location. Therefore, we hypothesize that the comparison between regional distributions of Hounsfield Unit (HU) values is relevant to discriminate between the considered ILD subtypes. For instance, typical UIP shows a spatial predominance of reticular abnormalities and honeycombing in the peripheral regions of the lung bases. Therefore, we expect a marked difference of HU distributions between the central and peripheral regions of the lung bases. Moreover, the construction of the graph leads to an interpretable patient descriptor. The descriptor led to encouraging area under the Receiver Operating Characteristic (ROC) curve in 0.6-0.8 for one-versus-one classification configurations, which also showed to outperform feature sets based on a simple concatenation of regional HU distributions.

SPIE Journal Paper | 2 November 2017

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging

Imon Banerjee, Sadhika Malladi, Daniela Lee, Adrien Depeursinge, Melinda Telli, Jafi Lipson, Daniel Golden, Daniel Rubin

JMI, Vol. 5, Issue 01, 011008, (November 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011008

KEYWORDS: Breast cancer, Performance modeling, Tumors, Magnetic resonance imaging, Data modeling, Wavelets, Statistical modeling, Image filtering, Statistical analysis, Breast

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Proceedings Article | 20 March 2015 Paper

Pulmonary embolism detection using localized vessel-based features in dual energy CT

Yashin Dicente Cid, Adrien Depeursinge, Antonio Foncubierta Rodríguez, Alexandra Platon, Pierre-Alexandre Poletti, Henning Müller

Proceedings Volume 9414, 941407 (2015) https://doi.org/10.1117/12.2082791

KEYWORDS: Prisms, Image segmentation, Lung, Computed tomography, Dual energy imaging, Signal attenuation, Performance modeling, Iodine, Visualization, Computer aided diagnosis and therapy

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Proceedings Article | 20 March 2015 Paper

Texture classification of anatomical structures in CT using a context-free machine learning approach

Oscar Jiménez del Toro, Antonio Foncubierta-Rodríguez, Adrien Depeursinge, Henning Müller

Proceedings Volume 9414, 94140W (2015) https://doi.org/10.1117/12.2082273

KEYWORDS: Visualization, Lung, Bladder, Spleen, Liver, Medical imaging, Information visualization, Visual analytics, Image segmentation, Matrices

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Proceedings Article | 26 September 2013 Paper

Rotation-covariant visual concept detection using steerable Riesz wavelets and bags of visual words

Adrien Depeursinge, Antonio Foncubierta, Henning Müller, Dimitri Van de Ville

Proceedings Volume 8858, 885816 (2013) https://doi.org/10.1117/12.2023806

KEYWORDS: Visualization, Visual process modeling, Wavelets, Data modeling, Image visualization, Principal component analysis, Visual analytics, Image processing, Radon, Sensors

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Proceedings Article | 29 March 2013 Paper

Region-based volumetric medical image retrieval

Antonio Foncubierta-Rodríguez, Henning Müller, Adrien Depeursinge

Proceedings Volume 8674, 867406 (2013) https://doi.org/10.1117/12.2007971

KEYWORDS: Medical imaging, Wavelets, Human-machine interfaces, Visualization, Image analysis, Sensors, Information visualization, Image retrieval, Lung, 3D image processing

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Proceedings Article | 16 February 2012 Paper

Synchronized slice viewing of similar image series

Sharib Ali, Antonio Foncubierta, Adrien Depeursinge, Fabrice Meriaudeau, Osman Ratib, Henning Müller

Proceedings Volume 8319, 83190K (2012) https://doi.org/10.1117/12.911233

KEYWORDS: Image registration, Lung, Brain, Neuroimaging, Cerebellum, Magnetic resonance imaging, Visualization, Image restoration, Computed tomography, Image analysis

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Comparing several series of images is not always easy as the corresponding slices often need to be selected manually. In times where series contain an ever-increasing number of slices this can mean manual work when moving several series to the corresponding slice. Particularly two situations were identified in this context: (1) patients with a large number of image series over time (such as patients with cancers that are monitored) frequently need to compare the series, for example to compare tumor growth over time. Manually adapting two series is possible but with four or more series this can mean loosing time. Having automatically the closest slice by comparing visual similarity also in older series with differing slice thickness and inter slice distance can save time and synchronize the viewing instantly. (2) analyzing visually similar image series of several patients can profit from being viewed in a synchronized way to compare the cases, so when sliding through the slices in one volume, the corresponding slices in the other volumes are shown. This application could be employed after content-based 3D image retrieval has found similar series, for example. Synchronized viewing can help finding or confirming the most relevant cases quickly. To allow for synchronized viewing of several image volumes, the test image series are first registered applying affine transformation for the global registration of images followed by diffeomorphic image registration. Then corresponding slices in the two volumes are estimated based on a visual similarity. Once the registration is finished, the user can subsequently move inside the slices of one volume (reference volume) and can view the corresponding slices in the other volumes. These corresponding slices are obtained after a correspondence match in the registration procedure. These volumes are synchronized in that the slice closest to the original reference volume is shown even when the slice thicknesses or inter slice distances differ, and this is automatically done by comparing the visual image content of the slices. The tool has the potential to help in a variety of situations and it is currently being made available as a plugin for the popular Osirix image viewer.

Proceedings Article | 14 February 2012 Paper

Three dimensional multi-scale visual words for texture-based cerebellum segmentation

Antonio Foncubierta-Rodríguez, Adrien Depeursinge, Laura Gui, Henning Müller

Proceedings Volume 8314, 83142Z (2012) https://doi.org/10.1117/12.911523

KEYWORDS: Cerebellum, Visualization, Image segmentation, Brain, Neuroimaging, Magnetic resonance imaging, Medical imaging, Image analysis, Wavelets, Image retrieval

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Proceedings Article | 25 March 2011 Paper

Multiscale salient point-based retrieval of fracture cases

Xin Zhou, Richard Stern, Adrien Depeursinge, Henning Müller

Proceedings Volume 7967, 796706 (2011) https://doi.org/10.1117/12.878046

KEYWORDS: Visualization, Bone, Databases, Feature extraction, Surgery, Information visualization, Image processing, X-rays, Performance modeling, Sensors

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Proceedings Article | 25 March 2011 Paper

Mobile medical image retrieval

Samuel Duc, Adrien Depeursinge, Ivan Eggel, Henning Müller

Proceedings Volume 7967, 79670G (2011) https://doi.org/10.1117/12.877054

KEYWORDS: Visualization, Information visualization, Databases, Image retrieval, Cameras, Operating systems, Medical imaging, Human-machine interfaces, Feature extraction, Radiology

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Images are an integral part of medical practice for diagnosis, treatment planning and teaching. Image retrieval has gained in importance mainly as a research domain over the past 20 years. Both textual and visual retrieval of images are essential. In the process of mobile devices becoming reliable and having a functionality equaling that of formerly desktop clients, mobile computing has gained ground and many applications have been explored. This creates a new field of mobile information search & access and in this context images can play an important role as they often allow understanding complex scenarios much quicker and easier than free text. Mobile information retrieval in general has skyrocketed over the past year with many new applications and tools being developed and all sorts of interfaces being adapted to mobile clients. This article describes constraints of an information retrieval system including visual and textual information retrieval from the medical literature of BioMedCentral and of the RSNA journals Radiology and Radiographics. Solutions for mobile data access with an example on an iPhone in a web-based environment are presented as iPhones are frequently used and the operating system is bound to become the most frequent smartphone operating system in 2011. A web-based scenario was chosen to allow for a use by other smart phone platforms such as Android as well. Constraints of small screens and navigation with touch screens are taken into account in the development of the application. A hybrid choice had to be taken to allow for taking pictures with the cell phone camera and upload them for visual similarity search as most producers of smart phones block this functionality to web applications. Mobile information access and in particular access to images can be surprisingly efficient and effective on smaller screens. Images can be read on screen much faster and relevance of documents can be identified quickly through the use of images contained in the text. Problems with the many, often incompatible mobile platforms were discovered and are listed in the text. Mobile information access is a quickly growing domain and the constraints of mobile access also need to be taken into account for image retrieval. The demonstrated access to the medical literature is most relevant as the medical literature and their images are clearly the largest knowledge source in the medical field.

Proceedings Article | 9 March 2011 Paper

3D lung image retrieval using localized features

Adrien Depeursinge, Tatjana Zrimec, Sata Busayarat, Henning Müller

Proceedings Volume 7963, 79632E (2011) https://doi.org/10.1117/12.877943

KEYWORDS: Lung, Tissues, Diagnostics, Image segmentation, Glasses, Feature extraction, Image retrieval, Visualization, 3D image processing, Chest

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Proceedings Article | 11 March 2008 Paper

A classification framework for lung tissue categorization

Adrien Depeursinge, Jimison Iavindrasana, Asmâa Hidki, Gilles Cohen, Antoine Geissbuhler, Alexandra Platon, Pierre-Alexandre Poletti, Henning Müller

Proceedings Volume 6919, 69190C (2008) https://doi.org/10.1117/12.769190

KEYWORDS: Lung, Tissues, Wavelets, Image classification, Feature extraction, Machine learning, Computing systems, Computer aided diagnosis and therapy, Data modeling, Java

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Proceedings Article | 31 March 2007 Paper

Image-based diagnostic aid for interstitial lung disease with secondary data integration

Adrien Depeursinge, Henning Müller, Asmâa Hidki, Pierre-Alexandre Poletti, Alexandra Platon, Antoine Geissbuhler

Proceedings Volume 6514, 65143P (2007) https://doi.org/10.1117/12.709533

KEYWORDS: Lung, Databases, Tissues, Diagnostics, Visualization, Chest, Medical imaging, Computed tomography, Data integration, Image classification

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Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image-based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer-based diagnostic decision support systems. In order to perform high-quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These features show good discriminative properties for the separation of five classes of visual observations.

Proceedings Article | 21 March 2007 Paper

Content-based image retrieval from a database of fracture images

Henning Müller, Phuong Anh Do Hoang, Adrien Depeursinge, Pierre Hoffmeyer, Richard Stern, Christian Lovis, Antoine Geissbuhler

Proceedings Volume 6516, 65160H (2007) https://doi.org/10.1117/12.709516

KEYWORDS: Databases, Feature extraction, Image retrieval, Surgery, Visualization, Classification systems, Prototyping, Image filtering, Medical imaging, Human-machine interfaces

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This article describes the use of a medical image retrieval system on a database of 16'000 fractures, selected from surgical routine over several years. Image retrieval has been a very active domain of research for several years. It was frequently proposed for the medical domain, but only few running systems were ever tested in clinical routine. For the planning of surgical interventions after fractures, x-ray images play an important role. The fractures are classified according to exact fracture location, plus whether and to which degree the fracture is damaging articulations to see how complicated a reparation will be. Several classification systems for fractures exist and the classification plus the experience of the surgeon lead in the end to the choice of surgical technique (screw, metal plate, ...). This choice is strongly influenced by the experience and knowledge of the surgeons with respect to a certain technique. Goal of this article is to describe a prototype that supplies similar cases to an example to help treatment planning and find the most appropriate technique for a surgical intervention. Our database contains over 16'000 fracture images before and after a surgical intervention. We use an image retrieval system (GNU Image Finding Tool, GIFT) to find cases/images similar to an example case currently under observation. Problems encountered are varying illumination of images as well as strong anatomic differences between patients. Regions of interest are usually small and the retrieval system needs to focus on this region. Results show that GIFT is capable of supplying similar cases, particularly when using relevance feedback, on such a large database. Usual image retrieval is based on a single image as search target but for this application we have to select images by case as similar cases need to be found and not images. A few false positive cases often remain in the results but they can be sorted out quickly by the surgeons. Image retrieval can well be used for the planning of operations by supplying similar cases. A variety of challenges has been identified and partly solved (varying luminosity, small region of interested, case-based instead of image-based). This article mainly presents a case study to identify potential benefits and problems. Several steps for improving the system have been identified as well and will be described at the end of the paper.

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