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- Kockelkorn, Thessa T J P, et al.
(författare)
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Interactive lung segmentation in abnormal human and animal chest CT scans.
- 2014
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Ingår i: Medical Physics. - : Wiley. - 0094-2405. ; 41:8, s. 417-429
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Tidskriftsartikel (refereegranskat)abstract
- Many medical image analysis systems require segmentation of the structures of interest as a first step. For scans with gross pathology, automatic segmentation methods may fail. The authors' aim is to develop a versatile, fast, and reliable interactive system to segment anatomical structures. In this study, this system was used for segmenting lungs in challenging thoracic computed tomography (CT) scans.
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- Gómez-de-Mariscal, E., et al.
(författare)
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DeepImageJ : A user-friendly environment to run deep learning models in ImageJ
- 2021
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Ingår i: Nature Methods. - : Springer Nature. - 1548-7091 .- 1548-7105. ; 18:10, s. 1192-1195
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Tidskriftsartikel (refereegranskat)abstract
- DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state of the art solutions and it is equipped with utility tools for developers to include new models. Very recently, several training frameworks have adopted the deepImageJ format to deploy their work in one of the most used softwares in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of deep learning models in life sciences applications and bioimage informatics.
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