Research Article
Year 2022,
, 145 - 154, 03.06.2022
Abstract
Multipl Miyelom, dünyada kansere bağlı ölümlerin yaklaşık %2’sine sebep olan bir hastalıktır. Bu hastalık nedeniyle normalde vücudun bağışıklık sistemi için antikor üreten plazma hücrelerinin sayısı kontrolsüz bir şekilde artmaktadır. Dolayısıyla plazma hücrelerin tespiti hastalığın teşhisi için önemli bir faktördür. Uzmanlar tarafından hastalığın teşhisi için kemik iliğinden örnekler alınarak kimyasal boyama teknikleriyle boyanmaktadır. Boyanan örneklerdeki plazma hücreleri mikroskopla incelenmektedir. Bu inceleme insan hatalarına açık olduğu gibi aynı zamanda çok zaman almaktadır. Bu çalışmada plazma hücrelerinin tespiti için otomatik bir sistem geliştirilmiştir. Plazma hücrelerinin tespiti için hücre çekirdeği ve sitoplazması farklı yöntemlerle ayrı ayrı segmente edilmiştir. Hücre çekirdeğine ait bölgeler Çok Seviyeli Eşikleme yöntemiyle, sitoplazması ise U-net evrişimsel sinir ağı kullanılarak segmente edilmiştir. Segmente edilen bölgeler uygulanan morfolojik işlemlerle iyileştirilmiştir. Segmente edilen çekirdek ve sitoplazma bölgelerinin birlikte değerlendirildiği görüntülerdeki her bir hücre için Çekirdek Hücre Oranı kriterine göre plazma hücreleri tespit edilmiştir. Veri setine ait 85 görüntü üzerinde önerilen yöntem uygulandığında, toplam 320 plazma hücresinden 279’u başarılı bir şekilde tespit edilmiştir. Çalışma sonucunda %87,19 duyarlılık, %74,6 kesinlik ve %80,4 F1-skor değerleri elde edilmiştir.
References
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Year 2022,
, 145 - 154, 03.06.2022
Abstract
References
- Afshin, B., Reza, A., Eman, S., & Alaa, S. (2021, September). Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images. In MICCAI Workshop on Computational Pathology (pp. 47-56). PMLR.
- Akay, B. (2013). A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Applied Soft Computing, 13(6), 3066-3091.
- Belevich, I., Joensuu, M., Kumar, D., Vihinen, H., & Jokitalo, E. (2016). Microscopy image browser: a platform for segmentation and analysis of multidimensional datasets. PLoS biology, 14(1), e1002340.
- Belevich, I., & Jokitalo, E. (2021). DeepMIB: user-friendly and open-source software for training of deep learning network for biological image segmentation. PLoS computational biology, 17(3), e1008374.
- Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057.
- Connolly, C., & Fleiss, T. (1997). A study of efficiency and accuracy in the transformation from RGB to CIELAB color space. IEEE transactions on image processing, 6(7), 1046-1048.
- Faura, Á. G., Štepec, D., Martinčič, T., & Skočaj, D. (2022, April). Segmentation of multiple myeloma plasma cells in microscopy images with noisy labels. In Medical Imaging 2022: Computer-Aided Diagnosis (Vol. 12033, pp. 160-167). SPIE.
- Gupta, R., Mallick, P., Duggal, R., Gupta, A., & Sharma, O. (2017). Stain color normalization and segmentation of plasma cells in microscopic images as a prelude to development of computer assisted automated disease diagnostic tool in multiple myeloma. Clinical Lymphoma, Myeloma and Leukemia, 17(1), e99.
- Gupta, A., Mallick, P., Sharma, O., Gupta, R., & Duggal, R. (2018). PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma. PloS one, 13(12), e0207908.
- Gupta, R., & Gupta, A. (2019). MiMM_SBILab Dataset: Microscopic Images of Multiple Myeloma [Data set]. The Cancer Imaging Archive.
- Gupta, A., Duggal, R., Gehlot, S., Gupta, R., Mangal, A., Kumar, L., Thakkar, N., & Satpathy, D. (2020). GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images. Medical Image Analysis, 65, 101788.
- Koç, A. B., & Akgün, D. (2021). U-net Mimarileri ile Glioma Tümör Segmentasyonu Üzerine Bir Literatür Çalışması. Avrupa Bilim ve Teknoloji Dergisi, (26), 407-414.
- Kuru, K. (2014). Optimization and enhancement of H&E stained microscopical images by applying bilinear interpolation method on lab color mode. Theoretical Biology and Medical Modelling, 11(1), 1-22.
- Merzban, M. H., & Elbayoumi, M. (2019). Efficient solution of Otsu multilevel image thresholding: A comparative study. Expert Systems with Applications, 116, 299-309.
- Paing, M. P., Sento, A., Bui, T. H., & Pintavirooj, C. (2022). Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN. Entropy, 24(1), 134.
- Qiu, X., Lei, H., Xie, H., & Lei, B. (2022, March). Segmentation of Multiple Myeloma Cells Using Feature Selection Pyramid Network and Semantic Cascade Mask RCNN. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.
- Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
- Saeedizadeh, Z., Mehri Dehnavi, A., Talebi, A., Rabbani, H., Sarrafzadeh, O., & Vard, A. (2016). Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM classifier. Journal of microscopy, 261(1), 46-56.
- Senthilkumaran, N., & Vaithegi, S. (2016). Image segmentation by using thresholding techniques for medical images. Computer Science & Engineering: An International Journal, 6(1), 1-13.
- Siddique, N., Paheding, S., Elkin, C. P., & Devabhaktuni, V. (2021). U-net and its variants for medical image segmentation: A review of theory and applications. IEEE Access.
- Taşgetiren, N. (2019). Renkli histopatolojik görüntüde meme kanseri hücre çekirdeği segmentasyonu (Master's thesis, KSÜ Fen Bilimleri Enstitüsü).
- Tuba, M. (2014). Multilevel image thresholding by nature-inspired algorithms: A short review. Computer Science Journal of Moldova, 66(3), 318-338.
- Vyshnav, M. T., Sowmya, V., Gopalakrishnan, E. A., & Menon, V. K. (2020, July). Deep learning based approach for multiple myeloma detection. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
There are 23 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Computer Engineering |
| Authors | |
| Publication Date | June 3, 2022 |
| Submission Date | May 24, 2022 |
| Published in Issue | Year 2022 |