3-DIMENSIONAL AUTOMATIC SEGMENTATION OF PITUARITY TUMOR USING DEEP LEARNING

Sinan Altun

doi:10.17780/ksujes.1422555

EN TR

3-DIMENSIONAL AUTOMATIC SEGMENTATION OF PITUARITY TUMOR USING DEEP LEARNING

Abstract

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.

Keywords

DERİN ÖĞRENME KULLANILARAK HİPOFİZ TÜMÖRÜNÜN 3 BOYUTLU OTOMATİK BÖLÜTLENMESİ

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

References

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Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Sinan Altun ^*
0000-0002-2356-0460
Türkiye

Publication Date

September 3, 2024

Submission Date

January 19, 2024

Acceptance Date

May 2, 2024

Published in Issue

Year 2024 Volume: 27 Number: 3

DOI

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

IZ

https://izlik.org/JA73DC86EF

Cite

RIS / Bibtex

APA

Altun, S. (2024). 3-DIMENSIONAL AUTOMATIC SEGMENTATION OF PITUARITY TUMOR USING DEEP LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 780-791. https://doi.org/10.17780/ksujes.1422555