Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Statistics for Spatial Data.

3D Slicer as an image computing platform for the Quantitative Imaging Network.

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.

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3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

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.

/pdf/computational-radiomics-system-to-decode-the-radiographic-qdly99paxt.pdf

Computational Radiomics System to Decode the Radiographic Phenotype

3D Slicer is an open-source platform for the analysis and display of information derived from medical imaging and similar data sets. Such advanced software environments are in daily use by researchers and clinicians and in many nonmedical applications. 3D Slicer is unique through serving clinical users, multidisciplinary clinical research terms, and software architects within a single technology structure and user community. Functions such as interactive visualization, image registration, and model-based analysis are now being complemented by more advanced capabilities, most notably in neurological imaging and intervention. These functions, originally limited to offline use by technical factors, are integral to large scale, rapidly developing research studies, and they are being increasingly integrated into the management and delivery of care. This activity has been led by a community of basic, applied, and clinical scientists and engineers, from both academic and commercial perspectives. 3D Slicer, a free open-source software package, is based in this community; 3D Slicer provides a set of interactive tools and a stable platform that can quickly incorporate new analysis techniques and evolve to serve more sophisticated real-time applications while remaining compatible with the latest hardware and software generations of host computer systems.

3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support

To be applied to practical clinical research problems, medical image computing software requires infrastructure including routines to read and write various file formats, manipulate 2D and 3D coordinate systems, and present a consistent user interface paradigm and visualization metaphor. At the same time, research software needs to be flexible to facilitate implementation of new ideas. 3D Slicer is a project that aims to provide a platform for a variety of applications through a community-development model. The resulting system has been used for research in both basic biomedical and clinically applied settings. 3D Slicer is built on a set of powerful and widely used software components (Tcl/Tk, VTK, ITK) to which is added an application layer that makes the system usable by non-programmer end-users. Using this approach, advanced applications including image guided surgery, robotics, brain mapping, and virtual colonoscopy have been implemented as 3D Slicer modules. In this paper we discuss some of the goals of the 3D Slicer project and how the architecture helps support those goals. We also point out some of the practical issues which arise from this approach.

3D Slicer

Medical image computing researchers often face the problem of moving promising new algorithms from the proof of concept stage into a form compatible with clinical use. Algorithm developers lack the time and resources to engineer their code for robustness and compatibility, while end-users are anxious to try new techniques but require well designed and tested user interfaces to make practical use of them. The NA-MIC Kit is a collection of software and methodology specifically designed to address these problems and facilitate the rapid advancement of the field.

Steve Pieper

Papers

3D Slicer as an image computing platform for the Quantitative Imaging Network.

Computational Radiomics System to Decode the Radiographic Phenotype

3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support

3D Slicer

The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community