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U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014).
Medical image segmentation is made difficult by low contrast, noise, and other imaging ambiguities. Although there are many computer vision techniques for image segmentation, some have been adapted specifically for medical image computing. Below is a sampling of techniques within this field; the implementation relies on the expertise that ...
Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other. The most common method for blob detection is by using convolution.
Studierfenster or StudierFenster (SF) [1] [2] [3] is a free, non-commercial open science client/server-based medical imaging processing online framework. It offers capabilities, like viewing medical data ( computed tomography (CT), magnetic resonance imaging (MRI), etc.) in two- and three-dimensional space directly in the standard web browsers ...
ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning ...
The model was called shift-invariant pattern recognition neural network before the name CNN was coined later in the early 1990s. Wei Zhang et al. also applied the same CNN without the last fully connected layer for medical image object segmentation (1991) [50] and breast cancer detection in mammograms (1994). [51]
3D Slicer (Slicer) is a free and open source software package for image analysis [1] [2] and scientific visualization. Slicer is used in a variety of medical applications, including autism , multiple sclerosis , systemic lupus erythematosus , prostate cancer , lung cancer , breast cancer , schizophrenia , orthopedic biomechanics , COPD ...
An image segmentation neural network can process small areas of an image to extract simple features such as edges. [81] Another neural network, or any decision-making mechanism, can then combine these features to label the areas of an image accordingly. A type of network designed this way is the Kohonen map.