Training Nuclei Detection Algorithms with Simple Annotations
Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection...
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| Published in: | Journal of pathology informatics Vol. 8; no. 1; p. 21 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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India
Elsevier Inc
01.01.2017
Wolters Kluwer India Pvt. Ltd Medknow Publications & Media Pvt. Ltd Medknow Publications & Media Pvt Ltd Elsevier |
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| ISSN: | 2153-3539, 2229-5089, 2153-3539 |
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| Abstract | Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets. |
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| AbstractList | Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible.BACKGROUNDGenerating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible.We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images.METHODSWe compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images.A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality.RESULTSA Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality.With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.CONCLUSIONSWith appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets. Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets. Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets. Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets. |
| ArticleNumber | 21 |
| Author | Molin, Jesper Hahn, Horst Karl Homeyer, André Kost, Henning Lundström, Claes |
| AuthorAffiliation | 4 Center for Medical Image Science and Visualization, Linköping University, 58183 Linköping, Sweden 1 Fraunhofer Institute for Medical Image Computing MEVIS, 28359 Bremen, Germany 2 Department of Applied Information Technology, Chalmers University of Technology, 41258 Gothenburg, Sweden 3 Sectra AB, 58330 Linköping, Sweden |
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| Author_xml | – sequence: 1 givenname: Henning surname: Kost fullname: Kost, Henning email: henning.kost@mevis.fraunhofer.de organization: Fraunhofer Institute for Medical Image Computing MEVIS, 28359 Bremen, Germany – sequence: 2 givenname: André surname: Homeyer fullname: Homeyer, André organization: Fraunhofer Institute for Medical Image Computing MEVIS, 28359 Bremen, Germany – sequence: 3 givenname: Jesper surname: Molin fullname: Molin, Jesper organization: Department of Applied Information Technology, Chalmers University of Technology, 41258 Gothenburg – sequence: 4 givenname: Claes surname: Lundström fullname: Lundström, Claes organization: Sectra AB, 58330 Linköping, Sweden – sequence: 5 givenname: Horst Karl surname: Hahn fullname: Hahn, Horst Karl organization: Fraunhofer Institute for Medical Image Computing MEVIS, 28359 Bremen, Germany |
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| CitedBy_id | crossref_primary_10_1016_j_jpi_2022_100011 crossref_primary_10_1016_j_neucom_2019_08_103 crossref_primary_10_1007_s11831_019_09366_4 crossref_primary_10_1111_his_14356 crossref_primary_10_3390_app13084842 crossref_primary_10_1016_j_compmedimag_2018_08_010 crossref_primary_10_1186_s12911_022_01826_5 |
| Cites_doi | 10.1002/path.2130 10.1016/j.neucom.2016.01.034 10.1186/1471-2105-15-9 10.4103/2153-3539.186902 10.1145/1007730.1007733 10.4103/2153-3539.92033 10.1109/TMI.2016.2525803 10.1200/JCO.2010.30.5037 10.1109/TMI.2015.2481436 10.1145/361002.361007 10.1097/PAS.0b013e318263207c 10.1111/jmi.12001 |
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| Copyright | 2017 The Authors Copyright Medknow Publications & Media Pvt. Ltd. 2017 Copyright: © 2017 Journal of Pathology Informatics 2017 |
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| Keywords | training set generation nuclei detection machine learning Active learning |
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| Snippet | Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour... Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to... Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour... |
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| SubjectTerms | Active learning Algorithms Annotations Breast cancer Cancer Classification Extraction Image detection Machine learning Markers Nuclei Nuclei detection Pathology Run time (computers) Teaching methods Tessellation Training Training set generation Visualization |
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| Title | Training Nuclei Detection Algorithms with Simple Annotations |
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