ConvNext Mixer-Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images

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dc.contributor.authorFirat, Hueseyin
dc.contributor.authorUzen, Hueseyin
dc.contributor.authorHanbay, Davut
dc.contributor.authorSengur, Abdulkadir
dc.date.accessioned2026-04-04T13:37:39Z
dc.date.available2026-04-04T13:37:39Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractHistopathology, vital in diagnosing medical conditions, especially in cancer research, relies on analyzing histopathology images (HIs). Nuclei segmentation, a key task, involves precisely identifying cell nuclei boundaries. Manual segmentation by pathologists is time-consuming, prompting the need for robust automated methods. Challenges in segmentation arise from HI complexities, necessitating advanced techniques. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have transformed nuclei segmentation. This study emphasizes feature extraction, introducing the ConvNext Mixer-based Encoder-Decoder (CNM-ED) model. Unlike traditional CNN based models, the proposed CNM-ED model enables the extraction of spatial and long context features to address the inherent complexities of histopathology images. This method leverages a multi-path strategy using a traditional CNN architecture as well as different paths focused on obtaining customized long context features using the ConvNext Mixer block structure that combines ConvMixer and ConvNext blocks. The fusion of these diverse features in the final segmentation output enables improved accuracy and performance, surpassing existing state-of-the-art segmentation models. Moreover, our multi-level feature extraction strategy is more effective than models using self-attention mechanisms such as SwinUnet and TransUnet, which have been frequently used in recent years. Experimental studies were conducted using five different datasets (TNBC, MoNuSeg, CoNSeP, CPM17, and CryoNuSeg) to analyze the performance of the proposed CNM-ED model. Comparisons were made with various CNN based models in the literature using evaluation metrics such as accuracy, AJI, macro F1 score, macro intersection over union, macro precision, and macro recall. It was observed that the proposed CNM-ED model achieved highly successful results across all metrics. Through comparisons with state-art-of models from the literature, the proposed CNM-ED model stands out as a promising advancement in nuclei segmentation, addressing the intricacies of histopathological images. The model demonstrates enhanced diagnostic capabilities and holds the potential for significant progress in medical research.
dc.identifier.doi10.1002/ima.23181
dc.identifier.issn0899-9457
dc.identifier.issn1098-1098
dc.identifier.issue5
dc.identifier.orcid0000-0002-1257-8518
dc.identifier.orcid0000-0002-0998-2130
dc.identifier.orcid0000-0003-2271-7865
dc.identifier.scopus2-s2.0-85204699583
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/ima.23181
dc.identifier.urihttps://hdl.handle.net/11616/109973
dc.identifier.volume34
dc.identifier.wosWOS:001317781800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal of Imaging Systems and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectConvMixer
dc.subjectConvNext
dc.subjecthistopathology images
dc.subjectnuclei segmentation
dc.titleConvNext Mixer-Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images
dc.typeArticle

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