Adaptive Rectangular and Feature‐Aware Mamba for Nuclei Segmentation in H&E Histopathology Images.
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| Title: | Adaptive Rectangular and Feature‐Aware Mamba for Nuclei Segmentation in H&E Histopathology Images. |
|---|---|
| Authors: | Wu, Nan1 (AUTHOR) black08211@outlook.com, Wang, Can2 (AUTHOR), Yu, Mingxing3 (AUTHOR) |
| Source: | International Journal of Imaging Systems & Technology. May2026, Vol. 36 Issue 3, p1-14. 14p. |
| Subjects: | Hematoxylin & eosin staining, Cell segmentation, Artificial neural networks, Computer-assisted image analysis (Medicine), Histopathology, Deep learning |
| Abstract: | Accurate nuclei segmentation in H&E histopathology underpins diagnosis, prognosis, and computational analysis, yet anisotropic shapes, stain/scanner variability, crowded overlaps, and multiscale context make robust delineation difficult. In this paper, we propose Adaptive Rectangular and Feature‐Aware Mamba Network (AFM‐Net), a moderate‐complexity encoder–decoder with skip connections for H&E nuclei segmentation. AFM‐Net integrates two complementary Mamba‐based modules: Adaptive Rectangular Mamba (AR‐Mamba), which estimates height/width‐specific kernel sizes and constructs a dynamic rectangular sampling grid with residual connections for anisotropy‐aware, scale‐adaptive features; and Lightweight Feature‐Aware Mamba (LFAM), which couples a variable‐selection scan with a channel Multilayer Perceptron (MLP) to efficiently process downsampled features while preserving long‐range dependencies in linear time and with a low memory footprint. A hybrid P–Dice loss balances pixel fidelity and region‐level overlap. Extensive experiments demonstrate the superiority of the proposed approach. Results on the publicly available CoNSeP and TNBC datasets show that AFM‐Net outperforms the baseline by 0.122/0.081 in Dice Similarity Coefficient (DSC) and 0.185/0.175 in Aggregated Jaccard Index (AJI), and reduces Hausdorff Distance (HD) by 2.840/2.260, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Imaging Systems & Technology is the property of Wiley-Blackwell 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194052883 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Rectangular and Feature‐Aware Mamba for Nuclei Segmentation in H&E Histopathology Images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Nan%22">Wu, Nan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> black08211@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Can%22">Wang, Can</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Mingxing%22">Yu, Mingxing</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Imaging+Systems+%26+Technology%22">International Journal of Imaging Systems & Technology</searchLink>. May2026, Vol. 36 Issue 3, p1-14. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hematoxylin+%26+eosin+staining%22">Hematoxylin & eosin staining</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+segmentation%22">Cell segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Histopathology%22">Histopathology</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate nuclei segmentation in H&E histopathology underpins diagnosis, prognosis, and computational analysis, yet anisotropic shapes, stain/scanner variability, crowded overlaps, and multiscale context make robust delineation difficult. In this paper, we propose Adaptive Rectangular and Feature‐Aware Mamba Network (AFM‐Net), a moderate‐complexity encoder–decoder with skip connections for H&E nuclei segmentation. AFM‐Net integrates two complementary Mamba‐based modules: Adaptive Rectangular Mamba (AR‐Mamba), which estimates height/width‐specific kernel sizes and constructs a dynamic rectangular sampling grid with residual connections for anisotropy‐aware, scale‐adaptive features; and Lightweight Feature‐Aware Mamba (LFAM), which couples a variable‐selection scan with a channel Multilayer Perceptron (MLP) to efficiently process downsampled features while preserving long‐range dependencies in linear time and with a low memory footprint. A hybrid P–Dice loss balances pixel fidelity and region‐level overlap. Extensive experiments demonstrate the superiority of the proposed approach. Results on the publicly available CoNSeP and TNBC datasets show that AFM‐Net outperforms the baseline by 0.122/0.081 in Dice Similarity Coefficient (DSC) and 0.185/0.175 in Aggregated Jaccard Index (AJI), and reduces Hausdorff Distance (HD) by 2.840/2.260, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Imaging Systems & Technology is the property of Wiley-Blackwell 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: 10.1002/ima.70369 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Hematoxylin & eosin staining Type: general – SubjectFull: Cell segmentation Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Computer-assisted image analysis (Medicine) Type: general – SubjectFull: Histopathology Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Adaptive Rectangular and Feature‐Aware Mamba for Nuclei Segmentation in H&E Histopathology Images. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Nan – PersonEntity: Name: NameFull: Wang, Can – PersonEntity: Name: NameFull: Yu, Mingxing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08999457 Numbering: – Type: volume Value: 36 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Imaging Systems & Technology Type: main |
| ResultId | 1 |
