Segmentation of Histopathological Images with LinkNet Model Supported by Vgg16 Backbone

Year 2025, Volume: 8 Issue: 1, 35 - 46, 30.06.2025

Furkan Atlan

https://doi.org/10.70030/sjmakeu.1658832

Abstract

Nuclei segmentation in histopathological images is crucial for the processing and analysis of medical images. Manual segmentation of nuclei images is challenging due to subjective errors by experts and image noise. Before the use of artificial intelligence in medical image analysis, segmentation tasks were performed with common classical methods such as thresholding and watershed. The development of deep learning has led to the emergence of models specifically designed for segmentation tasks. In this study, LinkNet model supported with Vgg16 backbone is proposed for segmenting histopathological images in CryoNuSeg dataset created for nucleus segmentation. After a small number of images are multiplied with data augmentation, feature maps are generated using the Vgg16 model integrated into the encoder of the LinkNet architecture. The results obtained in this study, with F1 Score, Intersection over Union (IoU), and Aggregated Jaccard Index (AJI) values of 0.8447, 0.7312, and 0.7312 respectively, demonstrate superior performance compared to recent studies utilizing the same dataset.

Keywords

Medical image segmentation , CryoNuSeg , backbone , Vgg16 , LinkNet

Thanks

The author gratefully acknowledges that a preliminary version of this paper appeared as an abstract in the IMISC2024 Conference Abstract Book.

References

• Van der Laak, J., Litjens, G., & Ciompi, F. (2021). Deep learning in histopathology: the path to the clinic. Nature medicine, 27(5), 775-784. https://doi.org/10.1038/s41591-021-01343-4
• Baidoshvili, A., Bucur, A., van Leeuwen, J., van der Laak, J., Kluin, P., & van Diest, P. J. (2018). Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics. Histopathology, 73(5), 784-794. https://doi.org/10.1111/his.13691
• Zheng, Y., Li, J., Shi, J., Xie, F., Huai, J., Cao, M., & Jiang, Z. (2023). Kernel attention transformer for histopathology whole slide image analysis and assistant cancer diagnosis. IEEE Transactions on Medical Imaging, 42(9), 2726-2739. https://doi.org/10.1109/TMI.2023.3264781
• Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A., & Balis, U. G. (2020). Digital pathology and computational image analysis in nephropathology. Nature Reviews Nephrology, 16(11), 669-685. https://doi.org/10.1038/s41581-020-0321-6
• Martos, O., Hoque, M. Z., Keskinarkaus, A., Kemi, N., Näpänkangas, J., Eskuri, M., ... & Seppänen, T. (2023). Optimized detection and segmentation of nuclei in gastric cancer images using stain normalization and blurred artifact removal. Pathology-Research and Practice, 248, 154694. https://doi.org/10.1016/j.prp.2023.154694
• Yildirim, Z., Samet, R., Hancer, E., Nemati, N., & Mali, M. T. (2023). Gland Segmentation in H&E Histopathological Images using U-Net with Attention Module. In 2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA), 1-6. https://doi.org/10.1109/IPTA59101.2023.10320014
• Irshad, H., Veillard, A., Roux, L., & Racoceanu, D. (2013). Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential. IEEE reviews in biomedical engineering, 7, 97-114. https://doi.org/10.1109/RBME.2013.2295804
• 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
• 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
• Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3431-3440.
• 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, 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
• Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, 3-11. https://doi.org/10.1007/978-3-030-00889-5_1
• Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, 424-432. https://doi.org/10.1007/978-3-319-46723-8_49
• Abedalla, A., Abdullah, M., Al-Ayyoub, M., & Benkhelifa, E. (2020). The 2ST-UNet for pneumothorax segmentation in chest X-Rays using ResNet34 as a backbone for U-Net. arXiv preprint arXiv:2009.02805.
• Sharma, N., & Gupta, S. (2023). Semantic Segmentation of Gastrointestinal Tract using UNet Model with ResNet 18 Backbone. In 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT), 226-230. https://doi.org/10.1109/APSIT58554.2023.10201739
• Das, N., & Das, S. (2024). Attention-UNet architectures with pretrained backbones for multi-class cardiac MR image segmentation. Current Problems in Cardiology, 49(1), 102129. https://doi.org/10.1016/j.cpcardiol.2023.102129
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
• Chaurasia, A., & Culurciello, E. (2017). Linknet: Exploiting encoder representations for efficient semantic segmentation. In 2017 IEEE visual communications and image processing (VCIP) , 1-4. https://doi.org/10.1109/VCIP.2017.8305148
• Long, F. (2020). Microscopy cell nuclei segmentation with enhanced U-Net. BMC bioinformatics, 21(1), 8. https://doi.org/10.1186/s12859-019-3332-1
• Lal, S., Das, D., Alabhya, K., Kanfade, A., Kumar, A., & Kini, J. (2021). NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images. Computers in Biology and Medicine, 128, 104075. https://doi.org/10.1016/j.compbiomed.2020.104075
• 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
• Qin, J., He, Y., Zhou, Y., Zhao, J., & Ding, B. (2022). REU-Net: Region-enhanced nuclei segmentation network. Computers in Biology and Medicine, 146, 105546. https://doi.org/10.1016/j.compbiomed.2022.105546
• 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
• Li, Z., Tang, Z., Hu, J., Wang, X., Jia, D., & Zhang, Y. (2023). Nst: a nuclei segmentation method based on transformer for gastrointestinal cancer pathological images. Biomedical Signal Processing and Control, 84, 104785. https://doi.org/10.1016/j.bspc.2023.104785
• Goceri, E. (2024). Nuclei segmentation using attention aware and adversarial networks. Neurocomputing, 579, 127445. https://doi.org/10.1016/j.neucom.2024.127445
• Hasan, M. J., Ahmad, W. S. H. M. W., Fauzi, M. F. A., & Abas, F. S. (2024). Hybrid Deep Learning Architectures for Histological Image Segmentation. In 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 75-80. https://doi.org/10.1109/ICAIIC60209.2024.10463355
• Hernández-García, A., & König, P. (2018). Data augmentation instead of explicit regularization. arXiv preprint arXiv:1806.03852.
• 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
• 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, 104349. https://doi.org/10.1016/j.compbiomed.2021.104349
• Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020). Random erasing data augmentation. In Proceedings of the AAAI conference on artificial intelligence, 34(7), 13001-13008. https://doi.org/10.1609/aaai.v34i07.7000
• Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, 448-456.
• Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), 807-814.
• Nijaguna, G. S., Babu, J. A., Parameshachari, B. D., de Prado, R. P., & Frnda, J. (2023). Quantum Fruit Fly algorithm and ResNet50-VGG16 for medical diagnosis. Applied Soft Computing, 136, 110055. https://doi.org/10.1016/j.asoc.2023.110055
• Sun, J., Yang, F., Cheng, J., Wang, S., & Fu, L. (2024). Nondestructive identification of soybean protein in minced chicken meat based on hyperspectral imaging and VGG16-SVM. Journal of Food Composition and Analysis, 125, 105713. https://doi.org/10.1016/j.jfca.2023.105713
• Zhang, J., Li, C., Kosov, S., Grzegorzek, M., Shirahama, K., Jiang, T., ... & Li, H. (2021). LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation. Pattern Recognition, 115, 107885. https://doi.org/10.1016/j.patcog.2021.107885
• 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. https://doi.org/10.1109/ICEIC54506.2022.9748537
• Liao, L., & Zhang, Z. (2023). Nuclear Segmentation Based on Recurrent Iteration and Fusion Attention Mechanism. In 2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS), 365-371. https://doi.org/10.1109/EIECS59936.2023.10435439
• Mahbod, A., Schaefer, G., Dorffner, G., Hatamikia, S., Ecker, R., & Ellinger, I. (2022). A dual decoder u-net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images. Frontiers in Medicine, 9, 978146. https://doi.org/10.3389/fmed.2022.978146
• Kadaskar, M., & Patil, N. (2023). Nuclei Segmentation using EfficientNetV2 and Convolutional Block Attention Module. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1-5. https://doi.org/10.1109/ICCCNT56998.2023.10307210
• Wang, Z., Zhang, Y., Wang, Y., Cai, L., & Zhang, Y. (2024). Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation. arXiv preprint arXiv:2406.16427.
• Chen, Z., Xu, Q., Liu, X., & Yuan, Y. (2024). UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images. arXiv preprint arXiv:2402.16663.
• Sun, X., Li, S., Chen, Y., Chen, J., Geng, H., Sun, K., ... & Zhang, H. (2025). Multiple Differential Convolution and Local-Variation Attention UNet: Nucleus Semantic Segmentation Based on Multiple Differential Convolution and Local-Variation Attention. Electronics, 14(6), 1058. https://doi.org/10.3390/electronics14061058
• Zhang, X., Xu, J., He, D., Wang, K., & Wang, L. (2025). Lightweight multi-scale attention group fusion structure for nuclei segmentation. The Journal of Supercomputing, 81(1), 199. https://doi.org/10.1007/s11227-024-06710-9
• Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022). Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1290-1299).
• Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., ... & Chen, K. (2022). Rtmdet: An empirical study of designing real-time object detectors. arXiv preprint arXiv:2212.07784. arXiv preprint arXiv:2212.07784 2022
• Ruan, H. (2024, December). 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
• Raza, S. E. A., Cheung, L., Shaban, M., Graham, S., Epstein, D., Plantaris, 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
• 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