Fully Automated Segmentation of Corpus Callosum Using Mask R-CNN on MR Images

YILDIRIM M. S., DANDIL E., Boru B.

5th International Conference on Intelligence-Based Transformations of Technology and Business Trends, ICITTBT 2025, Tirana, Arnavutluk, 29 - 30 Mayıs 2025, cilt.2669 CCIS, ss.140-154, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2669 CCIS
  • Doi Numarası: 10.1007/978-3-032-07373-0_10
  • Basıldığı Şehir: Tirana
  • Basıldığı Ülke: Arnavutluk
  • Sayfa Sayıları: ss.140-154
  • Anahtar Kelimeler: Corpus Callosum, Deep Learning, Mask R-CNN, Segmentation
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

Accurate segmentation of the corpus callosum (CC) in magnetic resonance imaging (MRI) plays a critical role in the diagnosis and monitoring of many neurological and psychiatric disorders. Manual segmentation is often time-consuming and subject to observer variability, highlighting the need for robust automated methods. In this research work, we have proposed a fully automated segmentation approach for the CC by using the Mask R-CNN deep learning architecture. The proposed model is trained and validated on a publicly available dataset of brain MR images to accurately localize and delineate the CC structure. Unlike traditional segmentation models, the proposed Mask R-CNN framework enables pixel-level instance segmentation with improved boundary accuracy. Experimental results show that the proposed method achieves a Dice Similarity Coefficient (DSC) of 94.8%, outperforming several deep learning models on the same task. These results demonstrate the effectiveness of the proposed instance segmentation approach in capturing the complex morphology of CC. The high accuracy and fully automated nature of the system suggest its potential as a valuable tool in clinical neuroimaging workflows for rapid and reproducible analysis of brain structure.