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HİSTOPATOLOJİK GÖRÜNTÜ ANALİZİNDE ÇEKİRDEK SEGMENTASYONU VE MİTOZ TESPİTİ İÇİN DERİN ÖĞRENME YÖNTEMLERİ

Yıl 2025, Cilt: 28 Sayı: 2, 785 - 801, 03.06.2025

Nooshin Nemati , Refik Samet , Emrah Hançer , Serpil Dizbay Sak , Ayca Bilge Kirmizi , Zeynep Yildirim

https://doi.org/10.17780/ksujes.1613789

Öz

Histopatolojik görüntü analizi, Hematoksilin ve Eozin (H&E) boyalı görüntülerden nicel bilgiler elde etmek için derin öğrenmeyi kullanan tıbbi araştırmaların önemli bir alanıdır. Bu çalışma, H&E ile boyanmış meme kanseri histopatoloji görüntülerinin analizini, çekirdekler ve mitoz üzerine odaklanmış derin öğrenme metodolojileri geliştirerek geliştirmeyi amaçlamaktadır. Çekirdekler, hastalık teşhisi için hayati bilgiler sağlarken, mitoz, kanser derecelendirmesi ve prognoz tahmini için kritik öneme sahiptir. İki metodoloji öneriyoruz: Birincisi, çekirdekleri CompSegNet adlı U-şekilli bir anlamsal segmentasyon mimarisi kullanarak segment emektedir; ikincisi ise mitotik hücreleri tespit edip sınıflandırmak için nesne tespiti ve bulanık sınıflandırma algoritmalarını birleştiren hibrit bir yaklaşım uygulamaktadır. Bu metodolojilerin etkinliğini değerlendirmek için, kamuya açık iki yeni veri seti sunuyoruz: NuSeC (Çekirdek Segmentasyonu ve Sınıflandırması) ve MiDeSeC (Mitoz Tespiti, Segmentasyonu ve Sınıflandırması). Bu veri setleri, yalnızca metodolojilerimizi doğrulamakla kalmayıp, aynı zamanda histopatolojik görüntü analizi için derin öğrenme modellerinin geliştirilmesine yönelik değerli kaynaklar sunmaktadır.

Anahtar Kelimeler

Meme kanseri dijital patoloji mitoz tespiti çekirdek segmentasyonu histopatoloji

Proje Numarası

121E379.16

Kaynakça

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DEEP LEARNING METHODOLOGIES FOR NUCLEI SEGMENTATION AND MITOSIS DETECTION IN HISTOPATHOLOGICAL IMAGES ANALYSIS

Yıl 2025, Cilt: 28 Sayı: 2, 785 - 801, 03.06.2025

Nooshin Nemati , Refik Samet , Emrah Hançer , Serpil Dizbay Sak , Ayca Bilge Kirmizi , Zeynep Yildirim

https://doi.org/10.17780/ksujes.1613789

Öz

Histopathological image analysis is a pivotal area of medical research that leverages deep learning to derive quantitative insights from Hematoxylin and Eosin (H\&E) stained images. This study aims to enhance the analysis of H\&E breast cancer histopathology images by developing deep learning methodologies focused on nuclei and mitosis. Nuclei provide essential information for disease diagnosis, while mitosis is crucial for cancer grading and prognosis prediction. We propose two methodologies: the first segments nuclei using a U-shaped semantic segmentation architecture called CompSegNet; the second detects and classifies mitotic cells through a hybrid approach combining object detection and fuzzy classification algorithms. To evaluate the effectiveness of these methodologies, we introduce two new publicly available datasets: NuSeC (Nuclei Segmentation and Classification) and MiDeSeC (Mitosis Detection, Segmentation, and Classification). These datasets not only validate our methodologies but also provide valuable resources for developing deep learning models in histopathological image analysis.

Anahtar Kelimeler

Breast cancer Digital pathology Mitosis detection Nuclei segmentation Histopathology.

Destekleyen Kurum

TUBİTAK

Proje Numarası

121E379.16

Teşekkür

This work is supported by Turkish Scientific and Research Council (TUBITAK) under Grant No.121E379.16

Kaynakça

  • Aatresh, A. A., Yatgiri, R. P., Chanchal, A. K., Kumar, A., Ravi, A., Das, D., & Kini, J. (2021). Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images. Computerized Medical Imaging and Graphics, 93, 101975. https://doi.org/10.1016/j.compmedimag.2021.101975.
  • Amgad, M., Atteya, L. A., Hussein, H., Mohammed, K. H., Hafiz, E., Elsebaie, M. A., & Cooper, L. A. (2022). NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer. GigaScience, 11, giac037. https://doi.org/10.1093/gigascience/giac037.
  • Aubreville, M., Stathonikos, N., Bertram, C. A., Klopfleisch, R., Ter Hoeve, N., Ciompi, F., & Breininger, K. (2023). Mitosis domain generalization in histopathology images—the MIDOG challenge. Medical Image Analysis, 84, 102699. https://doi.org/10.1016/j.media.2022.102699.
  • Aubreville, M., Wilm, F., Stathonikos, N., Breininger, K., Donovan, T. A., Jabari, S., & Bertram, C. A. (2023). A comprehensive multi-domain dataset for mitotic figure detection. Scientific data, 10(1), 484. https://doi.org/10.1038/s41597-023-02327-4.
  • Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., & Hamilton, P. W. (2017). QuPath: Open source software for digital pathology image analysis. Scientific reports, 7(1), 1-7. https://doi.org/10.1038/s41598-017-17204-5.
  • Bertram, C. A., Veta, M., Marzahl, C., Stathonikos, N., Maier, A., Klopfleisch, R., & Aubreville, M. (2020). Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels. In Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings 3, 204-213. Springer International Publishing. https://doi.org/10.1007/978-3-030-61166-8_22.
  • Bonissone, P., Cadenas, J. M., Garrido, M. C., & Díaz-Valladares, R. A. (2010). A fuzzy random forest. International Journal of Approximate Reasoning, 51(7), 729-747. https://doi.org/10.1016/j.ijar.2010.02.003.
  • Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), 801-818. https://doi.org/10.48550/arXiv.1802.02611.
  • Chen, Y., Li, X., Hu, K., Chen, Z., & Gao, X. (2020). Nuclei segmentation in histopathology images using rotation equivariant and multi-level feature aggregation neural network. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 549-554. IEEE. https://doi.org/10.1109/BIBM49941.2020.9313413.
  • Dodballapur, V., Song, Y., Huang, H., Chen, M., Chrzanowski, W., & Cai, W. (2019). Mask-driven mitosis detection in histopathology images. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 1855-1859. IEEE. https://doi.org/10.1109/ISBI.2019.8759164.
  • Gamper, J., Koohbanani, N. A., Benes, K., Graham, S., Jahanifar, M., Khurram, S. A., & Rajpoot, N. (2020). Pannuke dataset extension, insights and baselines. arXiv preprint arXiv:2003.10778. https://doi.org/10.48550/arXiv.2003.10778.
  • Graham, S., Jahanifar, M., Azam, A., Nimir, M., Tsang, Y. W., Dodd, K., & Rajpoot, N. M. (2021). Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification. In Proceedings of the IEEE/CVF international conference on computer vision, 684-693. https://doi.org/10.48550/arXiv.2108.11195.
  • 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.
  • Hamidinekoo, A., & Zwiggelaar, R. (2017). Stain colour normalisation to improve mitosis detection on breast histology images. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3, 213-221. Springer International Publishing. https://doi.org/10.1007/978-3-319-67558-9_25.
  • Hancer, E., Traore, 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.
  • Hassan, L., Abdel-Nasser, M., Saleh, A., A. Omer, O., & Puig, D. (2021). Efficient stain-aware nuclei segmentation deep learning framework for multi-center histopathological images. Electronics, 10(8), 954. https://doi.org/10.3390/electronics10080954.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778. https://doi.org/10.1109/CVPR.2016.90.
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  • Khan, M. S., Ali, S., Lee, Y. R., Kang, M. K., Park, S. Y., Tak, W. Y., & Jung, S. K. (2023). TransUNet-lite: A robust approach to cell nuclei segmentation. In Proceedings of the 2023 7th International Conference on Medical and Health Informatics, 251-258. https://doi.org/10.1145/3608298.3608344.
  • Khan, H. U., Raza, B., Shah, M. H., Usama, S. M., Tiwari, P., & Band, S. S. (2023). SMDetector: Small mitotic detector in histopathology images using faster R-CNN with dilated convolutions in backbone model. Biomedical Signal Processing and Control, 81, 104-414. https://doi.org/10.1016/j.bspc.2022.104414.
  • Khan, M. Z., Gajendran, M. K., Lee, Y., & Khan, M. A. (2021). Deep neural architectures for medical image semantic segmentation. IEEE Access, 9, 83002-83024. https://doi.org/10.1109/ACCESS.2021.3086530.
  • Kumar, N., Verma, R., Anand, D., Zhou, Y., Onder, O. F., Tsougenis, E., & Sethi, A. (2019). A multi-organ nucleus segmentation challenge. IEEE transactions on medical imaging, 39(5), 1380-1391. https://doi.org/10.1109/TMI.2019.2947628.
  • 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.
  • Le Dinh, T., Lee, S. H., Kwon, S. G., & Kwon, K. R. (2022). Cell nuclei segmentation in cryonuseg dataset using nested unet with efficientnet encoder. In 2022 International Conference on Electronics, Information, and Communication (ICEIC), 1-4. IEEE. https://doi.org/10.1109/ICEIC54506.2022.9748537.
  • Li, C., Wang, X., Liu, W., & Latecki, L. J. (2018). DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks. Medical image analysis, 45, 121-133. https://doi.org/10.1016/j.media.2017.12.002.
  • Liu, X., Guo, Z., Cao, J., & Tang, J. (2021). MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information. Computers in Biology and Medicine, 135, 104543. https://doi.org/10.1016/j.compbiomed.2021.104543.
  • Ludovic, R., Daniel, R., Nicolas, L., Maria, K., Humayun, I., Jacques, K., & Catherine, G. (2013). Mitosis detection in breast cancer histological images An ICPR 2012 contest. Journal of pathology informatics, 4(1), 8. https://doi.org/10.4103/2153-3539.112693.
  • Maarouf, C., Benomar, M. L., & Settouti, N. (2021). Pre-trained backbones effect on nuclei segmentation performance. In Mediterranean Conference on Pattern Recognition and Artificial Intelligence, 108-118. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-04112-9_8.
  • Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., & Thomas, N. E. (2009). A method for normalizing histology slides for quantitative analysis. In 2009 IEEE international symposium on biomedical imaging: from nano to macro, 1107-1110. IEEE. https://doi.org/10.1109/ISBI.2009.5193250.
  • Mahbod, A., Schaefer, G., Bancher, B., Löw, C., Dorffner, G., Ecker, R., & Ellinger, I. (2021). CryoNuSeg: A dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images. Computers in biology and medicine, 132, 104-349. https://doi.org/10.1016/j.compbiomed.2021.104349.
  • Maroof, N., Khan, A., Qureshi, S. A., ul Rehman, A., Khalil, R. K., & Shim, S. O. (2020). Mitosis detection in breast cancer histopathology images using hybrid feature space. Photodiagnosis and photodynamic therapy, 31, 101-885. https://doi.org/10.1016/j.pdpdt.2020.101885.
  • Naylor, P., Laé, M., Reyal, F., & Walter, T. (2017). Nuclei segmentation in histopathology images using deep neural networks. In 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), 933-936. IEEE. https://doi.org/10.1109/ISBI.2017.7950669.
  • 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.
  • Nemati, N., Samet, R., Hancer, E., Yildirim, Z., & Akkas, E. E. (2023). A Hybridized Deep Learning Methodology for Mitosis Detection and Classification from Histopathology Images. Journal of Machine Intelligence and Data Science (JMIDS), 4(1), 35-43. https://doi.org/10.11159/jmids.2023.005.
  • Obeid, A., Mahbub, T., Javed, S., Dias, J., & Werghi, N. (2022). NucDETR: end-to-end transformer for nucleus detection in histopathology images. In International Workshop on Computational Mathematics Modeling in Cancer Analysis, 47-57. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-17266-3_5.
  • Qin, J., He, Y., Zhou, Y., Zhao, J., & Ding, B. (2022). REU-Net: Region-enhanced nuclei segmentation network. Computers in Biology and Medicine, 146, 105-546. https://doi.org/10.1016/j.compbiomed.2022.105546.
  • 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, 3(18), 234-241. Springer International Publishing. https://doi.org/10.48550/arXiv.1505.04597.
  • Roux, L., Racoceanu, D., Capron, F., Calvo, J., Attieh, E., Le Naour, G., & Gloaguen, A. (2014). Mitos & atypia. Image Pervasive Access Lab (IPAL), Agency Sci., Technol. & Res. Inst. Infocom Res., Singapore, Tech. Rep, 1, 1-8.
  • Sebai, M. (2020). Improved SegMitos framework for mitosis detection in breast cancer histopathology images. In 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), 102-106. IEEE. https://doi.org/10.1109/ICAIIS49377.2020.9194877.
  • Sebai, M., Wang, T., & Al-Fadhli, S. A. (2020). PartMitosis: a partially supervised deep learning framework for mitosis detection in breast cancer histopathology images. IEEE Access, 8, 45133-45147. https://doi.org/10.1109/ACCESS.2020.2978754.
  • Sirinukunwattana, K., Raza, S. E. A., Tsang, Y. W., Snead, D. R., Cree, I. A., & Rajpoot, N. M. (2016). Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE transactions on medical imaging, 35(5), 1196-1206. https://doi.org/10.1109/TMI.2016.2525803.
  • Sohail, A., Khan, A., Wahab, N., Zameer, A., & Khan, S. (2021). A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images. Scientific Reports, 11(1), 6215. https://doi.org/10.1038/s41598-021-85652-1.
  • Swarts, D. R., van Suylen, R. J., den Bakker, M. A., van Oosterhout, M. F., Thunnissen, F. B., Volante, M., & Speel, E. J. M. (2014). Interobserver variability for the WHO classification of pulmonary carcinoids. The American journal of surgical pathology, 38(10), 1429-1436. https://doi.org/10.1097/PAS.0000000000000300.
  • 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.
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  • Verma, R., Kumar, N., Patil, A., Kurian, N. C., Rane, S., Graham, S., & Sethi, A. (2021). MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge. IEEE Transactions on Medical Imaging, 40(12), 3413-3423. https://doi.org/10.1109/TMI.2021.3085712.
  • Veta M, Viergever MA, Pluim JPW, Stathonikos N, van Diest PJ. MICCAI Grand Challenge: Assessment of mitosis detection algorithms (AMIDA13). Computer Vision and Pattern Recognition 2014. https://doi.org/10.1016/j.media.2014.11.010.
  • Vu, Q. D., Graham, S., Kurc, T., To, M. N. N., Shaban, M., Qaiser, T., & Farahani, K. (2019). Methods for segmentation and classification of digital microscopy tissue images. Frontiers in bioengineering and biotechnology, 7, 53. https://doi.org/10.3389/fbioe.2019.00053.
  • Wilm, F., Marzahl, C., Breininger, K., & Aubreville, M. (2021). Domain adversarial RetinaNet as a reference algorithm for the MItosis DOmain generalization challenge. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 5-13. Cham: Springer International Publishing. https://doi.org/10.48550/arXiv.2108.11269.
  • Wolberg, W. (1992). Breast cancer Wisconsin (original). UCI Machine Learning Repository, 110. https://doi.org/10.24432/C5HP4Z.
  • Yancey, R. (2023). Parallel YOLO-based Model for Real-time Mitosis Counting. 10.24132/CSRN.3201.32.
  • Yancey, R. E. (2022). Deep Feature Fusion for Mitosis Counting. In 2022 3rd International Conference on Pattern Recognition and Machine Learning (PRML), 151-158. IEEE. https://doi.org/10.1109/PRML56267.2022.9882245.
  • Yang, G., Huang, J., He, Y., Chen, Y., Wang, T., Jin, C., & Sengphachanh, P. (2022). GCP‐Net: A Gating Context‐Aware Pooling Network for Cervical Cell Nuclei Segmentation. Mobile Information Systems, 2022(1), 7511905. https://doi.org/10.1155/2022/7511905.
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Görüntü İşleme, Derin Öğrenme, Yapay Zeka (Diğer)
Bölüm Bilgisayar Mühendisliği
Yazarlar

Nooshin Nemati 0000-0002-5306-0344

Refik Samet 0000-0001-8720-6834

Emrah Hançer 0000-0002-3213-5191

Serpil Dizbay Sak 0000-0003-3666-3095

Ayca Bilge Kirmizi 0000-0003-3192-1921

Zeynep Yildirim 0000-0001-5846-9256

Proje Numarası 121E379.16
Yayımlanma Tarihi 3 Haziran 2025
Gönderilme Tarihi 5 Ocak 2025
Kabul Tarihi 18 Mart 2025
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 2

Kaynak Göster

APA Nemati, N., Samet, R., Hançer, E., Dizbay Sak, S., vd. (2025). DEEP LEARNING METHODOLOGIES FOR NUCLEI SEGMENTATION AND MITOSIS DETECTION IN HISTOPATHOLOGICAL IMAGES ANALYSIS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 785-801. https://doi.org/10.17780/ksujes.1613789
 Kapak Resmi İndir