Analyses of biomedical images often rely on demarcat-ing the boundaries of biological structures (segmentation). While numerous approaches are adopted to address the segmentation problem including collecting annotations from domain-experts and automated algorithms, the lack of comparative benchmarking makes it challenging to determine the current state-of-art, recognize limitations of existing approaches, and identify relevant future research directions. To provide practical guidance, we evalu-ated and compared the performance of trained experts, crowdsourced non-experts, and algorithms for annotating 305 objects coming from six datasets that include phase contrast, fluorescence, and magnetic resonance images. Compared to the gold standard ...
Collecting high quality annotations to construct an evaluation dataset is essential for assessing th...
The development of automatic methods for segmenting anatomy from medical images is an important goal...
To efficiently establish training databases for machine learning methods, collaborative and crowdsou...
High quality segmentations must be captured consistently for applications such as biomedical image a...
While traditional approaches to image analysis have typically relied upon either manual annotation b...
Deep learning has been applied successfully to many biomedical image segmentation tasks. However, du...
Currently, increasingly large medical imaging data sets become available for research and are analys...
Abstract. Currently, increasingly large medical imaging data sets be-come available for research and...
Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexper...
The biomedical imaging techniques grow rapidly and output big amount of data quickly in the recent y...
The availability of training data for supervision is a frequently encountered bottleneck of medical ...
International audienceThis chapter surveys basic segmentation techniques and point to tools that mak...
Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical i...
Semi-automated segmentation algorithms hold promise for improving extraction and identification of o...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
Collecting high quality annotations to construct an evaluation dataset is essential for assessing th...
The development of automatic methods for segmenting anatomy from medical images is an important goal...
To efficiently establish training databases for machine learning methods, collaborative and crowdsou...
High quality segmentations must be captured consistently for applications such as biomedical image a...
While traditional approaches to image analysis have typically relied upon either manual annotation b...
Deep learning has been applied successfully to many biomedical image segmentation tasks. However, du...
Currently, increasingly large medical imaging data sets become available for research and are analys...
Abstract. Currently, increasingly large medical imaging data sets be-come available for research and...
Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexper...
The biomedical imaging techniques grow rapidly and output big amount of data quickly in the recent y...
The availability of training data for supervision is a frequently encountered bottleneck of medical ...
International audienceThis chapter surveys basic segmentation techniques and point to tools that mak...
Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical i...
Semi-automated segmentation algorithms hold promise for improving extraction and identification of o...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
Collecting high quality annotations to construct an evaluation dataset is essential for assessing th...
The development of automatic methods for segmenting anatomy from medical images is an important goal...
To efficiently establish training databases for machine learning methods, collaborative and crowdsou...