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Manual labeling strategy for ground truth estimation in MRI glial tumor segmentation MRI神经胶质肿瘤分割中基底真值估计的人工标记策略
International Workshop on Video and Image Ground Truth in Computer Vision Applications Pub Date : 2012-05-21 DOI: 10.1145/2304496.2304504
V. Pedoia, A. Benedictis, Giuseppe Renis, E. Monti, S. Balbi, E. Binaghi
{"title":"Manual labeling strategy for ground truth estimation in MRI glial tumor segmentation","authors":"V. Pedoia, A. Benedictis, Giuseppe Renis, E. Monti, S. Balbi, E. Binaghi","doi":"10.1145/2304496.2304504","DOIUrl":"https://doi.org/10.1145/2304496.2304504","url":null,"abstract":"In this paper we focused our attention on the problem of determining reliable ground truth for validating unsupervised, fully automatic MRI brain tumor segmentation procedures in the clinical context of Glial Tumor treatment. The goal was achieved by proposing an integrated \"visual knowledge elicitation strategy\" centered on the use of GliMAn(Glial Tumor Manual Annotator), a 3D MRI navigator that allows to view and manually labeling MRI volumes. As seen in our experimental context, the manual labeling process benefits from the insertion of a software tool taylored on the experts visual and usability requirements.","PeriodicalId":196376,"journal":{"name":"International Workshop on Video and Image Ground Truth in Computer Vision Applications","volume":"22 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126216257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Combinatorial enlargement of ground-truth datasets and efficient evaluation of segmentation algorithms ground-truth数据集的组合扩展和分割算法的有效评价
International Workshop on Video and Image Ground Truth in Computer Vision Applications Pub Date : 2012-05-21 DOI: 10.1145/2304496.2304508
Akhil Shah, S. Dalal
{"title":"Combinatorial enlargement of ground-truth datasets and efficient evaluation of segmentation algorithms","authors":"Akhil Shah, S. Dalal","doi":"10.1145/2304496.2304508","DOIUrl":"https://doi.org/10.1145/2304496.2304508","url":null,"abstract":"We propose a method to exponentially enlarge a small dataset of domain specific ground truth segmentation labels to evaluate the performance of segmentation algorithms. Furthermore, we adapt ideas from combinatorial software testing to efficiently infer statistics of segmentation performance by evaluating performance on only a certain subset of the combinatorially generated images. Extensions of this work to optimal sequence for performance testing and algorithm selection are also suggested.","PeriodicalId":196376,"journal":{"name":"International Workshop on Video and Image Ground Truth in Computer Vision Applications","volume":"54 73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124704055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Efficient annotation of image data sets for computer vision applications 计算机视觉应用中图像数据集的高效标注
International Workshop on Video and Image Ground Truth in Computer Vision Applications Pub Date : 2012-05-21 DOI: 10.1145/2304496.2304498
Julia Möhrmann, G. Heidemann
{"title":"Efficient annotation of image data sets for computer vision applications","authors":"Julia Möhrmann, G. Heidemann","doi":"10.1145/2304496.2304498","DOIUrl":"https://doi.org/10.1145/2304496.2304498","url":null,"abstract":"High quality ground truth data sets are crucial for the development of image recognition systems. However, the task of annotating large image data sets manually takes a lot of time and effort. In order to lower the burden for the development of application-specific image recognition systems, we developed an advanced user interface. This interface is especially designed for non-expert users with little-to-no knowledge of computer vision techniques. The interface presents images clustered by similarity and allows for an efficient and simple annotation of large data sets. The integration of overview+detail concepts allows the precise navigation inside large data sets. The interface can be used without prior instructions on the underlying concepts, like self-organizing maps, image features or visualization techniques.","PeriodicalId":196376,"journal":{"name":"International Workshop on Video and Image Ground Truth in Computer Vision Applications","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126714637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 19
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