Segmentation of Pectoral Muscle Region in MLO Mammography Images by Backboned U-Net Mamografi Görüntülülerindeki Pektoral Kas Bölgesinin Omurgali U-Net ile Bölütlenmesi

Dogan R. O., Ture H., KAYIKÇIOĞLU T.

30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 15 - 18 Mayıs 2022 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu55565.2022.9864865
  • Basıldığı Şehir: Safranbolu
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Deep Learning, Deformed Pectoral Muscle, Mammography, MobileNetV2, U-Net
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.The pectoral muscle region on MLO mammography images appears prominently similar to suspicious areas. For this reason, Computer-Aided Detection (CAD) systems remove this region to reduce false-positive rates in the mass detection process. In some cases, the pectoral muscle region is exposed to distortions due to the superposition effects caused by the mammography technique. As a result, segmentation error rates of the pectoral muscle region, whose characteristic features are deteriorated, appear. In this study, a method to identify impaired pectoral muscle regions with MobileNetV2 backboned U-Net Deep Learning method is proposed. The proposed method was tested on 84 and 201 mammography images taken from both MIAS and InBreast databases and segmented with 1.81% and 1.92% false-negative (FN) and 0.25% and 0.37% false positive (FP) rates, respectively. Particularly for distorted pectoral muscle regions, the proposed method has been shown to outperform some pioneering studies in this area.