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Julien Finet

Bio: Julien Finet is an academic researcher from Kitware. The author has contributed to research in topics: Anatomy & Computer graphics (images). The author has an hindex of 2, co-authored 9 publications receiving 3187 citations.

Papers
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Journal ArticleDOI
TL;DR: An overview of 3D Slicer is presented as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications and the utility of the platform in the scope of QIN is illustrated.

4,786 citations

Journal ArticleDOI
TL;DR: What are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how the quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision are discussed.

56 citations

Proceedings ArticleDOI
TL;DR: The methodology is based on an intuitive port placement visualization module and atlas-based registration algorithm to transfer port locations to individual patients, and would decrease the amount of physician input necessary to optimize port placement for each patient case.
Abstract: Laparoscopic surgery is a minimally invasive surgical approach, in which abdominal surgical procedures are performed through trocars via small incisions. Patients benefit by reduced postoperative pain, shortened hospital stays, improved cosmetic results, and faster recovery times. Optimal port placement can improve surgeon dexterity and avoid the need to move the trocars, which would cause unnecessary trauma to the patient. We are building an intuitive open source visualization system to help surgeons identify ports. Our methodology is based on an intuitive port placement visualization module and atlas-based registration algorithm to transfer port locations to individual patients. The methodology follows three steps:1) Use a port placement visualization module to manually place ports in an abdominal organ atlas. This step generates port-augmented abdominal atlas. This is done only once for a given patient population. 2) Register the atlas data with the patient CT data, to transfer the prescribed ports to the individual patient 3) Review and adjust the transferred port locations using the port placement visualization module. Tool maneuverability and target reachability can be tested using the visualization system. Our methodology would decrease the amount of physician input necessary to optimize port placement for each patient case. In a follow up work, we plan to use the transferred ports as starting point for further optimization of the port locations by formulating a cost function that will take into account factors such as tool dexterity and likelihood of collision between instruments.

2 citations

01 Jan 2020
TL;DR: Preliminary results with a new annotation tool for liver volume and inner vessels from DCE-MRI data.
Abstract: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Preliminary results with a new annotation tool for liver volume and inner vessels from DCE-MRI data Jonas Lamy, Thibault Pelletier, Guillaume Lienemann, Benoît Magnin, Bertrand Kerautret, Nicolas Passat, Julien Finet, Antoine Vacavant

2 citations

Journal ArticleDOI
TL;DR: SlicerPET, a user-friendly workflow based module developed using open source software libraries to guide needle biopsy in the interventional suite, is presented.
Abstract: Biopsy is commonly used to confirm cancer diagnosis when radiologically indicated. Given the ability of PET to localize malignancies in heterogeneous tumors and tumors that do not have a CT correlate, PET/CT guided biopsy may improve the diagnostic yield of biopsies. To facilitate PET/CT guided needle biopsy, we developed a workflow that allows us to bring PET image guidance into the interventional CT suite. In this abstract, we present SlicerPET, a user-friendly workflow based module developed using open source software libraries to guide needle biopsy in the interventional suite.

1 citations


Cited by
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Book ChapterDOI
17 Oct 2016
TL;DR: In this paper, the authors propose a network for volumetric segmentation that learns from sparsely annotated volumetrized images, which is trained end-to-end from scratch, i.e., no pre-trained network is required.
Abstract: This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.

4,629 citations

Journal ArticleDOI
TL;DR: PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images, is developed and its application in characterizing lung lesions is demonstrated.
Abstract: Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. ©2017 AACR.

2,905 citations

Book ChapterDOI
01 Jan 2014
TL;DR: This chapter describes the basics of the DTI technique, outlines resources needed for acquisition, and focuses on post-processing techniques and statistical analyses with and without a priori hypotheses.
Abstract: Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging (MRI) technique that provides detailed information about tissue microstructure such as fiber orientation, axonal density, and degree of myelination. DTI is based on the measurement of the diffusion of water molecules. It was developed in the early 1990s and since then has been applied in a wide variety of scientific and clinical settings, especially, but not limited to, the investigation of brain pathology in schizophrenia (Shenton et al., Schizophr Res 49(1–2):1–52, 2001; Qiu et al., Neuroimage 47(4):1163–1171, 2009; Qiu et al., Neuroimage 52(4):1181–1189, 2010), Alzheimer’s disease (Damoiseaux et al., Hum Brain Mapp 30(4):1051–1059, 2009; Mielke et al. Neuroimage 46(1):47–55, 2009; Avants et al., Neuroimage 50(3):1004–1016, 2010; Gold et al., Neuroimage 52(4):1487–1494, 2010; Jahng et al., Neuroradiology 53(10):749–762, 2011)), and autism (Pugliese et al., Neuroimage 47(2):427–434, 2009; Cheng et al., Neuroimage 50(3):873–882, 2010; Fletcher et al., Neuroimage 51(3):1117–1125, 2010). This chapter describes the basics of the technique, outlines resources needed for acquisition, and focuses on post-processing techniques and statistical analyses with and without a priori hypotheses.

586 citations

Posted Content
TL;DR: The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts and performs on-the-fly elastic deformations for efficient data augmentation during training.
Abstract: This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.

539 citations

Journal ArticleDOI
TL;DR: This work represents a multi‐institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

473 citations