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HMSLIC / Hexagonal clustering and Morphological optimize Sequential Linear Iterative Clustering algorithm
Provides a superpixel segmentation algorithm focused on the lung CT images. HMSLIC uses a hexagonal method to obtain more regular segmented superpixels and improve edge information preservation. Besides, it uses morphology to combine bright superpixels to eliminate the influence of bright blob-like blood vessels with the aim of reducing the complexity of the subsequent calculation of distance.
DFCNet / Deep Fully Convolutional neural Network
Identifies and classifies the pulmonary nodules in CT-Scan images. DFCNet is a generic classifier built on deep fully convolutional neural network. The classification is separated into two classes of nodule (diseased-malignant of benign) and non-nodule (normal). Images classified as nodules are then categorized into four lung cancer stages, but this computer assisted detection (CAD) system can be used for the detection of other types of cancer because this software is a generic method for detection.
ITK / Insight Segmentation and Registration Toolkit
Allows to perform registration and segmentation for image analysis. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. ITK uses a model of software development known as Extreme Programming. The sampled representation is an image acquired from such medical instrumentation as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) or ultrasound scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.
DeepLung
New
Assists users in cancer diagnosis system. DeepLung is a fully automated lung computed tomography (CT) that combines nodule detection network and nodule classification network. It works in several steps: (i) for a CT image, the detection subnetwork detects candidate nodules, (ii) the classification subnetwork classifies detected nodules into either malignant or benign and (iii) the patient-level diagnosis result is achieved for the whole CT by fusing the diagnosis result of each nodule.
Saad2018
Serves as an automated delineation tool based on spatial analysis for non-small cell lung cancer (NSCLC). This algorithm intends to delineate lung nodules and larger NSCLC masses. It extracts regions of interest (ROI) regardless of the affected lobe side and can obtain low sensitivity values for endo-bronchial masses which are difficult to extract. In this case, the method only segments the nodule while ignoring the bronchus and unrelated pathological objects attached to the same branch.
Display
Allows users to display and manipulate three dimensional objects, mainly human cortical surfaces and sulcal curves. Display includes visualization and segmentation of 3D and 4D medical images. It supports a number of visualization features such as: visualization of 3D surfaces; the intersection of the 3D surface with the volumetric data; viewing an arbitrary, oblique plane through the volumetric data. It also permits researchers to annotate structural features on either a surface or a volumetric dataset.
Simpleware ScanIP
Provides a software environment for comprehensively processing 3D image data (MRI, CT, micro-CT, FIB-SEM…). Simpleware ScanIP offers powerful image visualisation, analysis, segmentation, and quantification tools. It includes video recording features and options to export surface models/meshes from segmented data for CAD and 3D printing. Additional modules are available for exporting CAE meshes, integrating image data and CAD, exporting NURBS and calculating effective material properties from scans.
Visible patient
Provides a list of tools to aid researchers in reading, interpreting, reporting, and treatment planning. Visible patient includes detection and labeling tools of organ segments. It contains basic imaging tools for: (1) general images; (2) including 2D viewing, (3) volume rendering and 3D volume viewing, (4) orthogonal Multi-Planar Reconstructions (MPR), (5) image fusion, (6) surface rendering, (7) measurements, (8) reporting, (9) storing, (10) general image management; and (11) administration.
BrainSeg3D
Provides a free volume (3D image) viewer and segmentation tool. BrainSeg3D is a graphic application that make segmentation of volumes more accurate by providing tools for semi-automated segmentation combined with a user friendly graphic interface. This application is based on Seg3D, a free volume segmentation and processing tool. It was developed for medical professionals who need to perform image analysis as part of their research or for researchers working in the field of image analysis.
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