Chestwall segmentation in 3D breast ultrasound using a deformable volume model

Abstract

A deformable volume segmentation method is proposed to detect the breast parenchyma in frontal scanned 3D whole breast ultrasound. Deformable volumes are a viable alternative to the deformable surface paradigm in noisy images with poorly defined object boundaries. A deformable ultrasound volume model was developed containing breast, rib, intercostal space and thoracic shadowing. Using prior knowledge about grey value statistics and shape the parameterized model deforms by optimization to match an ultrasound scan. Additionally a rib shadow enhancement filter was developed based on a Hessian sheet detector. An ROC chestwall detection study on 88 multi-center scans (20 non-visible chestwalls) showed a significant accuracy which improved strongly using the sheet detector. The results show the potential of our methodology to extract breast parenchyma which could help reduce false positives in subsequent computer aided lesion detection.