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IDX-EFFİCİENTUNET: HİSTOPATOLOJİK GÖRÜNTÜLERDE ÇEKİRDEK SEGMENTASYONU İÇİN INDEX-DRİVEN ETİKETLEME MEKANİZMASINA SAHİP EFFİCİENTNETB7 TABANLI U-NET YÖNTEMİ

Year 2025, Volume: 28 Issue: 3, 1427 - 1439, 03.09.2025

Furkan Atlan , Emrah Hançer

https://doi.org/10.17780/ksujes.1682089

Abstract

Histopatolojik görüntülerde hücre çekirdeklerinin doğru şekilde segmentasyonu, kanserin erken tanısı ve sınıflandırılması açısından büyük önem taşımaktadır. Bu çalışmada, U-Net temelli bir çekirdek segmentasyon modeli geliştirilmiş ve derin özellik çıkarımı için EfficientNetB7 kodlayıcı kullanılmıştır. Model, farklı çözünürlük seviyelerinden gelen bilgileri bütünleştirerek, karmaşık hücresel yapılardan anlamlı öznitelikler çıkarmakta ve segmentasyon doğruluğunu artırmaktadır. Ayrıca, eğitim verilerinde etiket tutarsızlıklarını ortadan kaldırmak üzere, maske sınıflarını otomatik olarak sayısal değerlere eşleyen “Index-Driven” adlı bir etiketleme mekanizması önerilmiştir. Bu yaklaşım, özellikle birden fazla kaynaktan elde edilen verilerin tekil sınıf temelli ikili segmentasyon için tutarlı biçimde hazırlanmasını sağlamaktadır. Gerçekleştirilen deneysel çalışmalar, bu mimari ve etiketleme bütünlüğünün, modelin segmentasyon doğruluğunu anlamlı düzeyde artırdığını ve literatürdeki yöntemlerle rekabet edebilecek bir performans sunduğunu göstermektedir.

Keywords

Çekirdek segmentasyonu U-Net EfficientNetB7 semantik segmentasyon dijital patoloji

References

  • Ahmad, I., Xia, Y., Cui, H., & Islam, Z. U. (2023). DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions. Expert Systems with Applications, 213, 118945. https://doi.org/10.1016/j.eswa.2022.118945
  • Basu, A., Senapati, P., Deb, M., Rai, R., & Dhal, K. G. (2024). A survey on recent trends in deep learning for nucleus segmentation from histopathology images. Evolving Systems, 15(1), 203-248. https://doi.org/10.1007/s12530-023-09491-3
  • Brixtel, R., Bougleux, S., Lézoray, O., Caillot, Y., Lemoine, B., Fontaine, M., ... & Renouf, A. (2022). Whole slide image quality in digital pathology: review and perspectives. IEEE Access, 10, 131005-131035. https://doi.org/10.1109/ACCESS.2022.3227437
  • Chen, J., Wang, R., Dong, W., He, H., & Wang, S. (2025). HistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification. BMC Medical Imaging, 25(1), 9. https://doi.org/10.1186/s12880-025-01550-2
  • Deshmukh, G., Susladkar, O., Makwana, D., & Mittal, S. (2022). FEEDNet: A feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis. Physics in Medicine & Biology, 67(19), 195011. https://doi.org/10.1088/1361-6560/ac8594
  • Dogar, G. M., Shahzad, M., & Fraz, M. M. (2023). Attention augmented distance regression and classification network for nuclei instance segmentation and type classification in histology images. Biomedical Signal Processing and Control, 79, 104199. https://doi.org/10.1016/j.bspc.2022.104199
  • Gabdullin, M. T., Mukasheva, A., Koishiyeva, D., Umarov, T., Bissembayev, A., Kim, K. S., & Kang, J. W. (2024). Automatic cancer nuclei segmentation on histological images: comparison study of deep learning methods. Biotechnology and Bioprocess Engineering, 29(6), 1034-1047. https://doi.org/10.1007/s12257-024-00130-5
  • Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019). Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Medical image analysis, 58, 101563. https://doi.org/10.1016/j.media.2019.101563
  • Guan, B., Chu, G., Wang, Z., Li, J., & Yi, B. (2025). Instance-level semantic segmentation of nuclei based on multimodal structure encoding. BMC bioinformatics, 26(1), 42. https://doi.org/10.1186/s12859-025-06066-8
  • Hancer, E., Traoré, M., Samet, R., Yıldırım, Z., & Nemati, N. (2023). An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images. Biomedical Signal Processing and Control, 83, 104720. https://doi.org/10.1016/j.bspc.2023.104720
  • Kadaskar, M., & Patil, N. (2024). ANet: Nuclei Instance Segmentation and Classification with Attention-Based Network. SN Computer Science, 5(4), 348. https://doi.org/10.1007/s42979-024-02661-3
  • Ke, J., Lu, Y., Shen, Y., Zhu, J., Zhou, Y., Huang, J., ... & Shen, D. (2023). Clusterseg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets. Medical Image Analysis, 85, 102758. https://doi.org/10.1016/j.media.2023.102758
  • Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., & Sethi, A. (2017). A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE transactions on medical imaging, 36(7), 1550-1560. https://doi.org/10.1109/TMI.2017.2677499
  • Liu, A., Zhang, Y., Xia, Y., Wan, X., Zhou, L., Song, W., ... & Yuan, X. (2024). Classes U-Net: A method for nuclei segmentation of photoacoustic histology imaging based on information entropy image classification. Biomedical Signal Processing and Control, 91, 105932. https://doi.org/10.1016/j.bspc.2023.105932
  • Moncayo, R., Martel, A. L., & Romero, E. (2023). Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods. Journal of Pathology Informatics, 14, 100315. https://doi.org/10.1016/j.jpi.2023.100315
  • Naylor, P., Laé, M., Reyal, F., & Walter, T. (2018). Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE transactions on medical imaging, 38(2), 448-459. https://doi.org/10.1109/TMI.2018.2865709
  • Nunes, J. D., Montezuma, D., Oliveira, D., Pereira, T., & Cardoso, J. S. (2024). A survey on cell nuclei instance segmentation and classification: Leveraging context and attention. Medical Image Analysis, 103360. https://doi.org/10.1016/j.media.2024.103360
  • Raza, S. E. A., Cheung, L., Shaban, M., Graham, S., Epstein, D., Pelengaris, S., ... & Rajpoot, N. M. (2019). Micro-Net: A unified model for segmentation of various objects in microscopy images. Medical image analysis, 52, 160-173. https://doi.org/10.1016/j.media.2018.12.003
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer international publishing. https://doi.org/10.1007/978-3-319-24574-4_28
  • Roy, A., Pramanik, P., Kaplun, D., Antonov, S., & Sarkar, R. (2024). AWGUNET: Attention-aided wavelet guided u-net for nuclei segmentation in histopathology images. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE. https://doi.org/10.1109/ISBI56570.2024.10635449
  • Ruan, H. (2024). A Color-Aware Unsupervised Segmentation Network for Nuclei in Histopathology Images. In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC) (pp. 627-631). IEEE. https://doi.org/10.1109/EIECC64539.2024.10929140
  • Senapati, P., Basu, A., Deb, M., & Dhal, K. G. (2024). Sharp dense u-net: an enhanced dense u-net architecture for nucleus segmentation. International Journal of Machine Learning and Cybernetics, 15(6), 2079-2094. https://doi.org/10.1007/s13042-023-02017-y
  • Shah, H. A., & Kang, J. M. (2023). An optimized multi-organ cancer cells segmentation for histopathological images based on CBAM-residual U-Net. IEEE Access, 11, 111608-111621. https://doi.org/10.1109/ACCESS.2023.3295914
  • Traoré, M., Hancer, E., Samet, R., Yıldırım, Z., & Nemati, N. (2024). CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images. Biomedical Signal Processing and Control, 97, 106699. https://doi.org/10.1016/j.bspc.2024.106699
  • Trinh, M. N., Nguyen, V. D., Pham, V. T., & Tran, T. T. (2023). An EffcientNet-encoder U-Net joint residual refinement module with Tversky–Kahneman Baroni–Urbani–Buser loss for biomedical image segmentation. Biomedical Signal Processing and Control, 83, 104631. https://doi.org/10.1016/j.bspc.2023.104631
  • Wang, T., Lei, Y., Fu, Y., Wynne, J. F., Curran, W. J., Liu, T., & Yang, X. (2021). A review on medical imaging synthesis using deep learning and its clinical applications. Journal of applied clinical medical physics, 22(1), 11-36. https://doi.org/10.1002/acm2.13121
  • Yuan, R., Zhang, W., Dong, X., & Zhang, W. (2025). Crns: CLIP-driven referring nuclei segmentation. The Journal of Supercomputing, 81(1), 174. https://doi.org/10.1007/s11227-024-06692-8
  • Yue, G., Ma, X., Li, W., An, Z., & Yang, C. (2025). 2MSPK-Net: A nuclei segmentation network based on multi-scale, multi-dimensional attention, and SAM prior knowledge. Biomedical Signal Processing and Control, 100, 107140. https://doi.org/10.1016/j.bspc.2024.107140
  • Zhou, Y., Onder, O. F., Dou, Q., Tsougenis, E., Chen, H., & Heng, P. A. (2019). Cia-net: Robust nuclei instance segmentation with contour-aware information aggregation. In Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings 26 (pp. 682-693). Springer International Publishing. https://doi.org/10.1007/978-3-030-20351-1_53

IDX-EFFICIENTUNET: AN EFFICIENTNETB7-ENHANCED U-NET METHOD WITH INDEX-DRIVEN LABELING FOR NUCLEI SEGMENTATION IN HISTOPATHOLOGY IMAGES

Year 2025, Volume: 28 Issue: 3, 1427 - 1439, 03.09.2025

Furkan Atlan , Emrah Hançer

https://doi.org/10.17780/ksujes.1682089

Abstract

Accurate segmentation of cell nuclei in histopathology images is essential for early cancer diagnosis and classification. In this study, we propose a U-Net-based segmentation model that incorporates an EfficientNetB7 encoder to enable deep feature extraction across multiple resolution levels. The architecture effectively captures complex cellular structures and enhances segmentation precision through rich multi-scale representations. To address label inconsistencies across datasets, we introduce an Index-Driven labeling mechanism that automatically maps semantic class annotations to numerical values. This strategy ensures consistent binary labeling, particularly when integrating heterogeneous mask sources into a unified training pipeline. Experimental results demonstrate that the integration of this architecture and labeling strategy significantly enhances the model’s segmentation accuracy and provides a performance that is competitive with existing methods in the literature.

Keywords

Nuclei segmentation U-Net EfficientNetB7 semantic segmentation digital pathology

References

  • Ahmad, I., Xia, Y., Cui, H., & Islam, Z. U. (2023). DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions. Expert Systems with Applications, 213, 118945. https://doi.org/10.1016/j.eswa.2022.118945
  • Basu, A., Senapati, P., Deb, M., Rai, R., & Dhal, K. G. (2024). A survey on recent trends in deep learning for nucleus segmentation from histopathology images. Evolving Systems, 15(1), 203-248. https://doi.org/10.1007/s12530-023-09491-3
  • Brixtel, R., Bougleux, S., Lézoray, O., Caillot, Y., Lemoine, B., Fontaine, M., ... & Renouf, A. (2022). Whole slide image quality in digital pathology: review and perspectives. IEEE Access, 10, 131005-131035. https://doi.org/10.1109/ACCESS.2022.3227437
  • Chen, J., Wang, R., Dong, W., He, H., & Wang, S. (2025). HistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification. BMC Medical Imaging, 25(1), 9. https://doi.org/10.1186/s12880-025-01550-2
  • Deshmukh, G., Susladkar, O., Makwana, D., & Mittal, S. (2022). FEEDNet: A feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis. Physics in Medicine & Biology, 67(19), 195011. https://doi.org/10.1088/1361-6560/ac8594
  • Dogar, G. M., Shahzad, M., & Fraz, M. M. (2023). Attention augmented distance regression and classification network for nuclei instance segmentation and type classification in histology images. Biomedical Signal Processing and Control, 79, 104199. https://doi.org/10.1016/j.bspc.2022.104199
  • Gabdullin, M. T., Mukasheva, A., Koishiyeva, D., Umarov, T., Bissembayev, A., Kim, K. S., & Kang, J. W. (2024). Automatic cancer nuclei segmentation on histological images: comparison study of deep learning methods. Biotechnology and Bioprocess Engineering, 29(6), 1034-1047. https://doi.org/10.1007/s12257-024-00130-5
  • Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019). Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Medical image analysis, 58, 101563. https://doi.org/10.1016/j.media.2019.101563
  • Guan, B., Chu, G., Wang, Z., Li, J., & Yi, B. (2025). Instance-level semantic segmentation of nuclei based on multimodal structure encoding. BMC bioinformatics, 26(1), 42. https://doi.org/10.1186/s12859-025-06066-8
  • Hancer, E., Traoré, M., Samet, R., Yıldırım, Z., & Nemati, N. (2023). An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images. Biomedical Signal Processing and Control, 83, 104720. https://doi.org/10.1016/j.bspc.2023.104720
  • Kadaskar, M., & Patil, N. (2024). ANet: Nuclei Instance Segmentation and Classification with Attention-Based Network. SN Computer Science, 5(4), 348. https://doi.org/10.1007/s42979-024-02661-3
  • Ke, J., Lu, Y., Shen, Y., Zhu, J., Zhou, Y., Huang, J., ... & Shen, D. (2023). Clusterseg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets. Medical Image Analysis, 85, 102758. https://doi.org/10.1016/j.media.2023.102758
  • Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., & Sethi, A. (2017). A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE transactions on medical imaging, 36(7), 1550-1560. https://doi.org/10.1109/TMI.2017.2677499
  • Liu, A., Zhang, Y., Xia, Y., Wan, X., Zhou, L., Song, W., ... & Yuan, X. (2024). Classes U-Net: A method for nuclei segmentation of photoacoustic histology imaging based on information entropy image classification. Biomedical Signal Processing and Control, 91, 105932. https://doi.org/10.1016/j.bspc.2023.105932
  • Moncayo, R., Martel, A. L., & Romero, E. (2023). Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods. Journal of Pathology Informatics, 14, 100315. https://doi.org/10.1016/j.jpi.2023.100315
  • Naylor, P., Laé, M., Reyal, F., & Walter, T. (2018). Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE transactions on medical imaging, 38(2), 448-459. https://doi.org/10.1109/TMI.2018.2865709
  • Nunes, J. D., Montezuma, D., Oliveira, D., Pereira, T., & Cardoso, J. S. (2024). A survey on cell nuclei instance segmentation and classification: Leveraging context and attention. Medical Image Analysis, 103360. https://doi.org/10.1016/j.media.2024.103360
  • Raza, S. E. A., Cheung, L., Shaban, M., Graham, S., Epstein, D., Pelengaris, S., ... & Rajpoot, N. M. (2019). Micro-Net: A unified model for segmentation of various objects in microscopy images. Medical image analysis, 52, 160-173. https://doi.org/10.1016/j.media.2018.12.003
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer international publishing. https://doi.org/10.1007/978-3-319-24574-4_28
  • Roy, A., Pramanik, P., Kaplun, D., Antonov, S., & Sarkar, R. (2024). AWGUNET: Attention-aided wavelet guided u-net for nuclei segmentation in histopathology images. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE. https://doi.org/10.1109/ISBI56570.2024.10635449
  • Ruan, H. (2024). A Color-Aware Unsupervised Segmentation Network for Nuclei in Histopathology Images. In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC) (pp. 627-631). IEEE. https://doi.org/10.1109/EIECC64539.2024.10929140
  • Senapati, P., Basu, A., Deb, M., & Dhal, K. G. (2024). Sharp dense u-net: an enhanced dense u-net architecture for nucleus segmentation. International Journal of Machine Learning and Cybernetics, 15(6), 2079-2094. https://doi.org/10.1007/s13042-023-02017-y
  • Shah, H. A., & Kang, J. M. (2023). An optimized multi-organ cancer cells segmentation for histopathological images based on CBAM-residual U-Net. IEEE Access, 11, 111608-111621. https://doi.org/10.1109/ACCESS.2023.3295914
  • Traoré, M., Hancer, E., Samet, R., Yıldırım, Z., & Nemati, N. (2024). CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images. Biomedical Signal Processing and Control, 97, 106699. https://doi.org/10.1016/j.bspc.2024.106699
  • Trinh, M. N., Nguyen, V. D., Pham, V. T., & Tran, T. T. (2023). An EffcientNet-encoder U-Net joint residual refinement module with Tversky–Kahneman Baroni–Urbani–Buser loss for biomedical image segmentation. Biomedical Signal Processing and Control, 83, 104631. https://doi.org/10.1016/j.bspc.2023.104631
  • Wang, T., Lei, Y., Fu, Y., Wynne, J. F., Curran, W. J., Liu, T., & Yang, X. (2021). A review on medical imaging synthesis using deep learning and its clinical applications. Journal of applied clinical medical physics, 22(1), 11-36. https://doi.org/10.1002/acm2.13121
  • Yuan, R., Zhang, W., Dong, X., & Zhang, W. (2025). Crns: CLIP-driven referring nuclei segmentation. The Journal of Supercomputing, 81(1), 174. https://doi.org/10.1007/s11227-024-06692-8
  • Yue, G., Ma, X., Li, W., An, Z., & Yang, C. (2025). 2MSPK-Net: A nuclei segmentation network based on multi-scale, multi-dimensional attention, and SAM prior knowledge. Biomedical Signal Processing and Control, 100, 107140. https://doi.org/10.1016/j.bspc.2024.107140
  • Zhou, Y., Onder, O. F., Dou, Q., Tsougenis, E., Chen, H., & Heng, P. A. (2019). Cia-net: Robust nuclei instance segmentation with contour-aware information aggregation. In Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings 26 (pp. 682-693). Springer International Publishing. https://doi.org/10.1007/978-3-030-20351-1_53
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence (Other)
Journal Section Computer Engineering
Authors

Furkan Atlan 0000-0003-1602-1941

Emrah Hançer 0000-0002-3213-5191

Publication Date September 3, 2025
Submission Date April 22, 2025
Acceptance Date July 17, 2025
Published in Issue Year 2025 Volume: 28 Issue: 3

Cite

APA Atlan, F., & Hançer, E. (2025). IDX-EFFİCİENTUNET: HİSTOPATOLOJİK GÖRÜNTÜLERDE ÇEKİRDEK SEGMENTASYONU İÇİN INDEX-DRİVEN ETİKETLEME MEKANİZMASINA SAHİP EFFİCİENTNETB7 TABANLI U-NET YÖNTEMİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1427-1439. https://doi.org/10.17780/ksujes.1682089
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