A boundary evidence controlled level set inference method for nuclei instance segmentation in histopathology images.
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| Title: | A boundary evidence controlled level set inference method for nuclei instance segmentation in histopathology images. |
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| Authors: | Vatani, Amir1 (AUTHOR), Song, Jie2 (AUTHOR), Xiao, Liang1 (AUTHOR) xiaoliang@mail.njust.edu.cn |
| Source: | Multimedia Tools & Applications. Aug2025, Vol. 84 Issue 27, p32985-33015. 31p. |
| Subjects: | Histopathology, Level set methods, Medical technology, Cell segmentation, Mathematical optimization, Splines, Image analysis |
| Abstract: | Periodic B-spline (PBS) has been a successfully used technique in histopathology image segmentation. However, it is limited when dealing with multi-instance objects in real-world biomedical applications. Moreover, its segmentation results are quite sensitive to the initial settings of the model and highly depend on the selection of control points. To address these issues and boost the canonical PBS to a new level of adaptive fitting, we present Level-Set B-Spline (LSBS), a novel definition of nuclear contour inference that employs B-splines under the energy minimization of a variational level set functional, which can promote each other. To this end, we adopt a two-stage approach: The first stage solves the marker detection and image segmentation problem and the second stage solves the joint modeling of the missing contour and the latent shape. To do so, we propose the use of modified concavity measurement and optimal boundary-to-marker association as an efficient basis for computing a periodic set of knots to drive the LSBS. For any probe image, our approach enables to analysis of free-lying and overlapping cell nuclei with weak boundaries. We demonstrated the superiority of the proposed LSBS by evaluating it on various datasets, including the challenging TCGA KIRC and Kumar nuclei, where the consistently state-of-the-art performances were achieved for comparison against state-of-the-art image analysis-based methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 187118476 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A boundary evidence controlled level set inference method for nuclei instance segmentation in histopathology images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vatani%2C+Amir%22">Vatani, Amir</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Jie%22">Song, Jie</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiao%2C+Liang%22">Xiao, Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xiaoliang@mail.njust.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Aug2025, Vol. 84 Issue 27, p32985-33015. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Histopathology%22">Histopathology</searchLink><br /><searchLink fieldCode="DE" term="%22Level+set+methods%22">Level set methods</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+technology%22">Medical technology</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+segmentation%22">Cell segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Splines%22">Splines</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Periodic B-spline (PBS) has been a successfully used technique in histopathology image segmentation. However, it is limited when dealing with multi-instance objects in real-world biomedical applications. Moreover, its segmentation results are quite sensitive to the initial settings of the model and highly depend on the selection of control points. To address these issues and boost the canonical PBS to a new level of adaptive fitting, we present Level-Set B-Spline (LSBS), a novel definition of nuclear contour inference that employs B-splines under the energy minimization of a variational level set functional, which can promote each other. To this end, we adopt a two-stage approach: The first stage solves the marker detection and image segmentation problem and the second stage solves the joint modeling of the missing contour and the latent shape. To do so, we propose the use of modified concavity measurement and optimal boundary-to-marker association as an efficient basis for computing a periodic set of knots to drive the LSBS. For any probe image, our approach enables to analysis of free-lying and overlapping cell nuclei with weak boundaries. We demonstrated the superiority of the proposed LSBS by evaluating it on various datasets, including the challenging TCGA KIRC and Kumar nuclei, where the consistently state-of-the-art performances were achieved for comparison against state-of-the-art image analysis-based methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-20515-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 32985 Subjects: – SubjectFull: Histopathology Type: general – SubjectFull: Level set methods Type: general – SubjectFull: Medical technology Type: general – SubjectFull: Cell segmentation Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Splines Type: general – SubjectFull: Image analysis Type: general Titles: – TitleFull: A boundary evidence controlled level set inference method for nuclei instance segmentation in histopathology images. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vatani, Amir – PersonEntity: Name: NameFull: Song, Jie – PersonEntity: Name: NameFull: Xiao, Liang IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 27 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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