Mask R-CNN kullanılarak yeni bir MRG veri tabanında prostat bölgelerinin segmentasyonu: PACS sistemi üzerinde bir uygulama

Yıl 2024, Cilt: 39 Sayı: 3, 1401 - 1416, 20.05.2024

Çağlar Gürkan , Abdulkadir Budak , Hakan Karataş , Kayıhan Akın

https://doi.org/10.17341/gazimmfd.1153507

Öz

Akciğer kanserinden sonra erkeklerde en yaygın rastlanan kanser türü prostat kanseridir. Günümüzde, ileri prostat görüntüleme radyologlar tarafından yapılan multiparametrik prostat manyetik rezonans görüntüleme (MRG) ile gerçekleştirilmektedir. Prostatın birçok patolojisi görüntülenebilse de, asıl amaç prostat kanseri olasılığını belirlemek ve biyopsi işlemine gerek olup olmadığına karar vermektir. Bu sürece, T2 ağırlıklı görüntüler (T2W), difüzyon ağırlıklı görüntüler (DWI) ve dinamik kontrastlı görüntüler (DCE) olmak üzere farklı seriler halindeki MRG görüntülerinin analizi dahil edilmektedir. Bununla birlikte, öncelikle prostat bölgelerinin ayrıştırılması gerekmektedir. Daha sonra ilgili prostat bölgelerinde lezyon taraması yapılmaktadır. Son olarak ise prostat lezyon skorlama işleminin PI-RADS v2’ye göre yapılmasına ihtiyaç duyulmaktadır. Bu nedenle prostat kanseri tanısının konulması karışık ve uzun bir süreçtir. Bu sebeble, prostat kanseri tanısının koyulması için karar destek sistemlerine ihtiyaç duyulmaktadır. Bu bağlamda, çalışmanın başlıca amacı prostat bölgelerinin otomatik olarak segmentasyonunu sağlamaktır. Segmentasyon görevinde 15 hastaya ait T2W MRG görüntüleri ile birlikte Mask R-CNN algoritması kullanılmıştır. Mask R-CNN algoritması ResNet-50 omurga modelinin kullanımı ile birlikte 96,040 mAP50 değeri ile segmentasyon performansı elde etmiştir. Son olarak, eğitilen model PACS sistemine entegre edilmiştir. Entegrasyon sayesinde hastanelerde kullanıma hazır bir yapay zeka destekli karar destek sistemi geliştirilmiştir. Böylelikle, sağlık çalışanları üzerindeki iş yükü azaltılırken zamandan da kazanç sağlanmıştır.

Anahtar Kelimeler

Prostat bölge segmentasyonu, prostat kanseri, T2W MRG, PI-RADS v2, derin öğrenme

Destekleyen Kurum

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)

Proje Numarası

3191419

Teşekkür

Bu proje, 3191419 proje numarası ile Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmektedir. Bu projenin¸ yürütülmesinde her türlü olanağı sağlayan Akgün Bilgisayar’a teşekkür ederiz.

Segmentation of prostate zones on a novel MRI database using Mask R-CNN: an implementation on PACS system

Öz

After lung cancer, prostate cancer is the most common type of cancer in men. Nowadays, advanced prostate imaging is conducted by multiparametric prostate magnetic resonance imaging (MRI) performed by radiologists. Although many pathologies of the prostate can be visualized, the main purpose is to determine the probability of prostate cancer and to decide whether a biopsy is needed. This process includes the analysis of different series of Magnetic Resonance (MR) images, including T2-weighted images (T2W), diffuse-weighted images (DWI), and dynamic contrast enhanced images (DCE). However, firstly it is necessary to differentiate the prostate zones. Then, lesion scanning is performed in the relevant prostate zones. Finally, there is a need to perform prostate lesion scoring according to PI-RADS v2. Therefore, diagnosis of prostate cancer is a complex and long process. For this reason, decision support systems are needed for the diagnosis of prostate cancer. In this context, the main purpose of the study is to provide automatic segmentation of prostate regions. In the segmentation task, Mask R-CNN algorithm was used with T2W MR images of 15 patients. With the use of the ResNet-50 backbone model, the Mask R-CNN algorithm achieved the segmentation performance with a mAP50 value of 96.040. Finally, the trained model was integrated into the PACS system. Thanks to the integration, a ready-to-use in hospitals artificial intelligence-supported decision support system was developed. Thus, time was saved while reducing the workload on health employees.

Anahtar Kelimeler

Prostate zone segmentation, prostate cancer, T2W MRI, PI-RADS v2, deep learning

Proje Numarası

3191419

Kaynakça

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