3-DIMENSIONAL AUTOMATIC SEGMENTATION OF PITUARITY TUMOR USING DEEP LEARNING

Yıl 2024, Cilt: 27 Sayı: 3, 780 – 791, 03.09.2024

Sinan Altun

https://doi.org/10.17780/ksujes.1422555

Öz

İyi huylu bir hipofiz tümörünün gelişimi çok yavaş ilerler. Bu yavaş gelişme nedeniyle hastaya tanı konulması zaman alabilir. Birçok hormonun salgılanmasında etkili olan ve görme sinirlerinin arkasında yer alan Hipofiz Bezinde oluşacak tümör, Hipofiz Bezinin 2/3’ünü kaplayabilir. Tümörün büyümesine bağlı olarak sinirler üzerindeki baskı arttıkça bazı belirtiler ortaya çıkar. Hormonal dengenin önemli olduğu kişilerde Hipofiz Tümörü nedeniyle adet düzensizliği, görme bozuklukları, baş ağrısı, anne sütü üretiminde dengesizlik, ACTH fazla üretimi sonucu Cushing sendromu hastalıkları görülebilmektedir. Aşırı ACTH aşırı kilo alımına, kırılgan kemik yapısının ortaya çıkmasına, ciltte yara izlerine ve duygusal değişikliklere neden olabilir. Hipofiz Tümörü beynin en derin kısmında yer alır ve burada cerrahi operasyon yapılması zordur. Derin öğrenme tekniklerini kullanan anlamsal bölümleme başarılı olabilir. Çalışmamız ile Tümörün %98’e varan IoU skoruyla otomatik segmentasyonu mümkün oldu. Bu başarı nispeten yüksektir ve Akciğer tümörleri için oluşturulacak CAD sistemi için umut vaat etmektedir. Son zamanlarda geliştirilen 3D-Unet tekniği, 3 boyutlu otomatik segmentasyon yapabilmektedir. Bu çalışma, karmaşık bir operasyon gerektiren Hipofiz Tümörünün 3D-Unet modeli ile otomatik olarak segmentasyonunu amaçlamaktadır.

Anahtar Kelimeler

Hipofiz tümörü, hormonal hastalıklar, semantik bölütleme, 3B-Unet, derin öğrenme

Kaynakça

• Afshari, M., Yang, A., Bega, D. (2017). Motivators and Barriers to Exercise in Parkinson’s Disease. Journal of Parkinson’s Disease, 7(4), 703–711. https://doi.org/10.3233/jpd-171173
• Alkan, F., Ersoy, B., Kızılay, D. Ö., Ozyurt, B. C., Coskun, S. (2022). Evaluation of cardiac structure, exercise capacity, and electrocardiography parameters in children with partial and complete growth hormone deficiency and their changes with short-term growth hormone replacement therapy. Pituitary, 26(1), 115–123. https://doi.org/10.1007/s11102-022-01295-z
• Alqudah, A. M., Alquraan, H., Abu-Qasmieh, I., Alqudah, A., Al-Sharu, W. (2019). Brain Tumor Classification Using Deep Learning Technique – A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes. International Journal of Advanced Trends in Computer Science and Engineering, 8(6), 3684–3691. https://doi.org/10.30534/ijatcse/2019/155862019
• Altun, S., Alkan, A. (2022c). LSS‐net: 3‐dimensional segmentation of the spinal canal to diagnose lumbar spinal stenosis. International Journal of Imaging Systems and Technology, 33(1), 378–388. https://doi.org/10.1002/ima.22807
• Ciavarra, B., McIntyre, T., Kole, M. J., Li, W., Yao, W., Guttenberg, K. B., Blackburn, S. L. (2023). Antiplatelet and anticoagulation therapy and the risk of pituitary apoplexy in pituitary adenoma patients. Pituitary. https://doi.org/10.1007/s11102-023-01316-5
• Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In Lecture Notes in Computer Science (pp. 424–432). Springer Science+Business Media. https://doi.org/10.1007/978-3-319-46723-8_49
• Egger, J., Zukić, D., Freisleben, B., Kolb, A., Nimsky, C. (2013). Segmentation of pituitary adenoma: A graph-based method vs. a balloon inflation method. Computer Methods and Programs in Biomedicine, 110(3), 268–278. https://doi.org/10.1016/j.cmpb.2012.11.010
• Geer, E. B. (2023). Medical therapy for refractory pituitary adenomas. Pituitary. https://doi.org/10.1007/s11102-023-01320-9
• Qian, Y., Qiu, Y., Li, C., Wang, Z., Cao, B., Huang, H., Ni, Y., Chen, L., Sun, J. (2020). A novel diagnostic method for pituitary adenoma based on magnetic resonance imaging using a convolutional neural network. Pituitary. https://doi.org/10.1007/s11102-020-01032-4
• Sharma, M., Wang, D., Scott, V., Ugiliweneza, B., Potts, K., Savage, J., Boakye, M., Andaluz, N., Williams, B. J. (2023). Intraoperative MRI use in transsphenoidal surgery for pituitary tumors: Trends and healthcare utilization. Journal of Clinical Neuroscience, pp. 111, 86–90. https://doi.org/10.1016/j.jocn.2023.03.009
• Simander, G., Dahlqvist, P., Oja, L., Eriksson, P. O., Lindvall, P., Koskinen, L. D. (2023). Intrasellar pressure is related to endocrine disturbances in patients with pituitary tumors. World Neurosurgery. https://doi.org/10.1016/j.wneu.2023.03.085
• Song, H., Yoon, H., Lee, S., Hong, C., Yi, B. (2019). Surgical Navigation System for Transsphenoidal Pituitary Surgery Applying U-Net-Based Automatic Segmentation and Bendable Devices. Applied Sciences, 9(24), 5540. https://doi.org/10.3390/app9245540
• Srinivasa Reddy. K, Jaya. T. (2021). Medical Image Retrieval using Two-Dimensional PCA. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(4), 1852-1856. https://doi.org/10.35940/ijrte.D1152.018520
• Staartjes, V. E., Serra, C., Muscas, G., Maldaner, N., Akeret, K., Van Niftrik, C. H. B., Fierstra, J., Holzmann, D., Regli, L. (2018). Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study. Neurosurgical Focus, 45(5), E12. https://doi.org/10.3171/2018.8.focus18243
• Trimpou, P., Backlund, E., Ragnarsson, O., Skoglund, T., T, H., Gudnadottir, G., Carlqvist, J., Farahmand, D. (2022). Long-Term Outcomes and Complications from Endoscopic Versus Microscopic Transsphenoidal Surgery for Cushing’s Disease: A 15-Year Single-Center Study. World Neurosurgery, 166, e427–e434. https://doi.org/10.1016/j.wneu.2022.07.027
• Tsuneoka, H., Tosaka, M., Yamaguchi, R., Tanaka, Y., Mukada, N., Shimauchi-Ohtaki, H., Aihara, M., Shimizu, T., Yoshimoto, Y. (2023). The Significance of the Intercarotid Distances for Transsphenoidal Pituitary Surgery: A Magnetic Resonance Imaging Study. World Neurosurgery. https://doi.org/10.1016/j.wneu.2023.04.009
• Du, Y., Zhang, S., Fang, Y., Qiu, Q., Zhao, L., Wei, W., Tang, Y., & Li, X. (2022). Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer's Disease. Frontiers in Aging Neuroscience, (), n/a.
• Almufareh, F.M., Imran, M., Khan, A., Humayun, M., Asim, M. (2024). Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning. IEEE Access. 12, 16189 – 16207. DOI: 10.1109/ACCESS.2024.3359418

3-DIMENSIONAL AUTOMATIC SEGMENTATION OF PITUARITY TUMOR USING DEEP LEARNING

Yıl 2024, Cilt: 27 Sayı: 3, 780 – 791, 03.09.2024

Sinan Altun

https://doi.org/10.17780/ksujes.1422555

Öz

The development of a benign pituitary tumor progresses very slowly. Due to this slow development, it may take time to diagnose the patient. The Tumor that will form in the Pituitary Gland, which is effective in the secretion of many hormones and located behind the optic nerves, may cover 2/3 of the Pituitary Gland. In people for whom hormonal balance is essential, due to Pituitary Tumor, Cushing’s syndrome diseases can be seen as a result of irregular menstruation, visual disturbances, headache, imbalance in breast milk production, and excess ACTH production. Excess ACTH can lead to excessive weight gain, the appearance of fragile bone structure, skin scars, and emotional changes. The Pituitary Tumor is located in the deepest part of the brain, and it is tough to perform a surgical operation there. Semantic segmentation using deep learning techniques can be successful. With our study, automatic segmentation of the Tumor with an IoU score of up to 98% was possible. This success is relatively high, and promises hope for the CAD system to be created for Pulmonary tumors. The 3D-Unet technique developed recently, can perform automatic segmentation in 3 dimensions. This study aims to automatically segment a Pituitary Tumor, which requires a complex operation, with the 3D-Unet model.

Anahtar Kelimeler

Pituarity tumor, hormonal dieases, semantic segmentation, 3D-UNet, deep learning

Kaynakça

• Afshari, M., Yang, A., Bega, D. (2017). Motivators and Barriers to Exercise in Parkinson’s Disease. Journal of Parkinson’s Disease, 7(4), 703–711. https://doi.org/10.3233/jpd-171173
• Alkan, F., Ersoy, B., Kızılay, D. Ö., Ozyurt, B. C., Coskun, S. (2022). Evaluation of cardiac structure, exercise capacity, and electrocardiography parameters in children with partial and complete growth hormone deficiency and their changes with short-term growth hormone replacement therapy. Pituitary, 26(1), 115–123. https://doi.org/10.1007/s11102-022-01295-z
• Alqudah, A. M., Alquraan, H., Abu-Qasmieh, I., Alqudah, A., Al-Sharu, W. (2019). Brain Tumor Classification Using Deep Learning Technique – A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes. International Journal of Advanced Trends in Computer Science and Engineering, 8(6), 3684–3691. https://doi.org/10.30534/ijatcse/2019/155862019
• Altun, S., Alkan, A. (2022c). LSS‐net: 3‐dimensional segmentation of the spinal canal to diagnose lumbar spinal stenosis. International Journal of Imaging Systems and Technology, 33(1), 378–388. https://doi.org/10.1002/ima.22807
• Ciavarra, B., McIntyre, T., Kole, M. J., Li, W., Yao, W., Guttenberg, K. B., Blackburn, S. L. (2023). Antiplatelet and anticoagulation therapy and the risk of pituitary apoplexy in pituitary adenoma patients. Pituitary. https://doi.org/10.1007/s11102-023-01316-5
• Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In Lecture Notes in Computer Science (pp. 424–432). Springer Science+Business Media. https://doi.org/10.1007/978-3-319-46723-8_49
• Egger, J., Zukić, D., Freisleben, B., Kolb, A., Nimsky, C. (2013). Segmentation of pituitary adenoma: A graph-based method vs. a balloon inflation method. Computer Methods and Programs in Biomedicine, 110(3), 268–278. https://doi.org/10.1016/j.cmpb.2012.11.010
• Geer, E. B. (2023). Medical therapy for refractory pituitary adenomas. Pituitary. https://doi.org/10.1007/s11102-023-01320-9
• Qian, Y., Qiu, Y., Li, C., Wang, Z., Cao, B., Huang, H., Ni, Y., Chen, L., Sun, J. (2020). A novel diagnostic method for pituitary adenoma based on magnetic resonance imaging using a convolutional neural network. Pituitary. https://doi.org/10.1007/s11102-020-01032-4
• Sharma, M., Wang, D., Scott, V., Ugiliweneza, B., Potts, K., Savage, J., Boakye, M., Andaluz, N., Williams, B. J. (2023). Intraoperative MRI use in transsphenoidal surgery for pituitary tumors: Trends and healthcare utilization. Journal of Clinical Neuroscience, pp. 111, 86–90. https://doi.org/10.1016/j.jocn.2023.03.009
• Simander, G., Dahlqvist, P., Oja, L., Eriksson, P. O., Lindvall, P., Koskinen, L. D. (2023). Intrasellar pressure is related to endocrine disturbances in patients with pituitary tumors. World Neurosurgery. https://doi.org/10.1016/j.wneu.2023.03.085
• Song, H., Yoon, H., Lee, S., Hong, C., Yi, B. (2019). Surgical Navigation System for Transsphenoidal Pituitary Surgery Applying U-Net-Based Automatic Segmentation and Bendable Devices. Applied Sciences, 9(24), 5540. https://doi.org/10.3390/app9245540
• Srinivasa Reddy. K, Jaya. T. (2021). Medical Image Retrieval using Two-Dimensional PCA. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(4), 1852-1856. https://doi.org/10.35940/ijrte.D1152.018520
• Staartjes, V. E., Serra, C., Muscas, G., Maldaner, N., Akeret, K., Van Niftrik, C. H. B., Fierstra, J., Holzmann, D., Regli, L. (2018). Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study. Neurosurgical Focus, 45(5), E12. https://doi.org/10.3171/2018.8.focus18243
• Trimpou, P., Backlund, E., Ragnarsson, O., Skoglund, T., T, H., Gudnadottir, G., Carlqvist, J., Farahmand, D. (2022). Long-Term Outcomes and Complications from Endoscopic Versus Microscopic Transsphenoidal Surgery for Cushing’s Disease: A 15-Year Single-Center Study. World Neurosurgery, 166, e427–e434. https://doi.org/10.1016/j.wneu.2022.07.027
• Tsuneoka, H., Tosaka, M., Yamaguchi, R., Tanaka, Y., Mukada, N., Shimauchi-Ohtaki, H., Aihara, M., Shimizu, T., Yoshimoto, Y. (2023). The Significance of the Intercarotid Distances for Transsphenoidal Pituitary Surgery: A Magnetic Resonance Imaging Study. World Neurosurgery. https://doi.org/10.1016/j.wneu.2023.04.009
• Du, Y., Zhang, S., Fang, Y., Qiu, Q., Zhao, L., Wei, W., Tang, Y., & Li, X. (2022). Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer's Disease. Frontiers in Aging Neuroscience, (), n/a.
• Almufareh, F.M., Imran, M., Khan, A., Humayun, M., Asim, M. (2024). Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning. IEEE Access. 12, 16189 – 16207. DOI: 10.1109/ACCESS.2024.3359418

Toplam 18 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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