The percentage variability of each structure segmented by operator O1 (solid line) and O2 (dotted line). It can be noted that the lowest variability was observed for both operators for the largest and more defined brain structures, thus suggesting that the MRI volume of the segmented structure is influenced by the actual size and its intrinsic contrast with the surrounding parenchyma. The ventricles have been segmented through an automatic approach (one segmentation—no variability).</p
<p>Examples of segmentation variability among operators A (yellow), B (red), and C (green) for a few...
The automation of segmentation of medical images is an active research area. However, there has been...
Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality c...
Contains fulltext : 284106.pdf (Publisher’s version ) (Open Access
Automated gray matter segmentation of magnetic resonance imaging data is essential for morphometric ...
Automated gray matter segmentation of magnetic resonance imaging data is essential for morphometric ...
The purpose of this work was to characterize expert variation in segmentation of intracranial struct...
Evaluation of neurodegenerative disease progression may be assisted by quantification of the volume ...
The results for the ventricles refer to the automatic segmentation. A) Volume variation between the ...
<p>Segmentation1 (yellow), Segmentation2 (red), and Segmentation3 (green) are provided for operators...
he le to ed ts a it me characterization of abnormalities are still a challenging and difficult task ...
This memo describes the stability testing of the TINA medical image segmentation algorithm de-scribe...
Whisker plot for the distribution of the segmented volumes from the two operators O1 (black) and O2 ...
For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully auto...
Brain tumor analysis is moving towards volumetric assessment of magnetic resonance imaging (MRI), pr...
<p>Examples of segmentation variability among operators A (yellow), B (red), and C (green) for a few...
The automation of segmentation of medical images is an active research area. However, there has been...
Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality c...
Contains fulltext : 284106.pdf (Publisher’s version ) (Open Access
Automated gray matter segmentation of magnetic resonance imaging data is essential for morphometric ...
Automated gray matter segmentation of magnetic resonance imaging data is essential for morphometric ...
The purpose of this work was to characterize expert variation in segmentation of intracranial struct...
Evaluation of neurodegenerative disease progression may be assisted by quantification of the volume ...
The results for the ventricles refer to the automatic segmentation. A) Volume variation between the ...
<p>Segmentation1 (yellow), Segmentation2 (red), and Segmentation3 (green) are provided for operators...
he le to ed ts a it me characterization of abnormalities are still a challenging and difficult task ...
This memo describes the stability testing of the TINA medical image segmentation algorithm de-scribe...
Whisker plot for the distribution of the segmented volumes from the two operators O1 (black) and O2 ...
For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully auto...
Brain tumor analysis is moving towards volumetric assessment of magnetic resonance imaging (MRI), pr...
<p>Examples of segmentation variability among operators A (yellow), B (red), and C (green) for a few...
The automation of segmentation of medical images is an active research area. However, there has been...
Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality c...