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Loos les Lille, France

Dolz J.,AQUILAB | Dolz J.,Lille University Hospital Center | Massoptier L.,AQUILAB | Vermandel M.,Lille University Hospital Center

This work covers the current state of the art with regard to approaches to segment subcortical brain structures. A huge range of diverse methods have been presented in the literature during the last decade to segment not only one or a constrained number of structures, but also a complete set of these subcortical regions. Special attention has been paid to atlas based segmentation methods, statistical models and deformable models for this purpose. More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem. These methods are presented in this work, and their advantages and drawbacks are further discussed. Although these methods have proved to perform well, their use is often limited to those situations where either there are no lesions in the brain or the presence of lesions does not highly vary the brain anatomy. Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice by some clinical applications, such as radiotherapy or radiosurgery. © 2015 Elsevier Masson SAS. Source

Dolz J.,AQUILAB | Dolz J.,Lille University Hospital Center | Laprie A.,Institute Claudius Regaud | Ken S.,Institute Claudius Regaud | And 6 more authors.
International Journal of Computer Assisted Radiology and Surgery

Purpose: To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). Methods: SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Results: Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm3, where the value for best performing IIVs configuration was 0.85 cm3, representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Conclusion: Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use. © 2015, CARS. Source

Agency: Cordis | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-2011-ITN | Award Amount: 3.45M | Year: 2012

The greatest challenge for radiation therapy is to reach the highest probability of cure with the least morbidity. In practice, some difficulties remain to identify cancer cells, target them with radiation and minimize collateral damage. Over the last decades, remarkable progress has been made thanks to modern advances in computer and imaging technologies. Currently, the radiotherapy has reached a point where, besides 3D tumour morphology, time variations and biological variability within the tumour can also be taken into account. The SUMMER project is devised to produce unique software using several imaging sources (CT, MRI, PET, MR spectroscopy, fMRI, 4D PET-CT) for biological target volume delineation, based on spatial co-registration of multimodal morphologic and functional images. Furthermore, it will make additional biological information concerning tumour extension and tumour response available to radiotherapy, essential for patient treatment follow-up. Finally, radiation area will be more focused leading to less side-effect for the patient. Radiation oncology is now more dependent on medical imaging than it has ever been - and that dependence is only going to become greater. Therefore, convergence and collaboration of radiation oncology, nuclear medicine, diagnostic imaging and also computer science is the underlying driver to integrate efficiently and cost-effectively all information coming from various imaging technologies into the radiation therapy workflow. The design challenge is to combine the different level and kind of information into one interface, while currently doctors need to mentally do this operation. SUMMER will contribute to renew and strengthen this relationship through cross-disciplinary research, common workshops, and collaboration on training and education.

Servagi Vernat S.,Besancon University Hospital Center | Servagi Vernat S.,University of Franche Comte | Ali D.,University of Paris Descartes | Puyraveau M.,Besancon University Hospital Center | And 6 more authors.
Physica Medica

Background: Intensity Modulated Arc Therapy (IMAT) can be planned and delivered via several techniques. Advanced Radiotherapy (ARTORL) is a prospective study that aims to evaluate the treatment costs and clinical aspects of implementing these IMAT techniques for head and neck cancers. In this context, we evaluated the potential dosimetric gain of Helical Tomotherapy (TomoTherapy, Accuray, HT) versus VMAT (Rapid'Arc®, Varian Medical System, RA) for oropharyngeal cancer (OC). Material and methods: Thirty patients were selected from our database in whom bilateral neck irradiation and treatment to the primary were indicated. Each patient was planned twice using both HT and RA planning systems using a simultaneous integrated boost approach. For the planning target volumes (PTV) and organs at risk, ICRU 83 reporting guidelines were followed. RA and HT plans were compared using paired Student's t-test. Results: RA and HT produced plans with a good coverage of PTVs and acceptable sparing of OARs. Although some dosimetric differences were statistically significant, they remained small. However, the near maximal dose to the PRV of spinal cord and brain stem was lower with HT. Regarding normal tissue, HT increased the volume irradiated at doses between 4 and 20Gy compared to RA. Conclusion: In OC, HT and RA showed similar dosimetric results. They represent the maximum gains obtained with photon beams. The medicoeconomic evaluation of our study is ongoing and may reveal differences between these techniques in terms of MU number, fraction time, and clinical evaluation. © 2013 Associazione Italiana di Fisica Medica. Source

Dolz J.,AQUILAB | Dolz J.,Lille University Hospital Center | Kirisli H.A.,AQUILAB | Fechter T.,Albert Ludwigs University of Freiburg | And 5 more authors.
Medical Physics

Purpose: Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Methods: Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Results: Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. Conclusions: An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs required for thoracic radiation therapy has been presented and clinically evaluated. The introduction of the proposed system in clinical routine may offer valuable new option to radiation oncologists in performing RTP. © 2016 American Association of Physicists in Medicine. Source

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