Purpose: Automated 3D breast ultrasound (ABUS) has been proposed as a complementary screening
modality to mammography for early detection of breast cancers. To facilitate the interpretation
of ABUS images, automated diagnosis and detection techniques are being developed, in which
malignant lesion segmentation plays an important role. However, automated segmentation of cancer
in ABUS is challenging since lesion edges might not be well defined. In this study, the authors aim
at developing an automated segmentation method for malignant lesions in ABUS that is robust to
ill-defined cancer edges and posterior shadowing.
Methods: A segmentation method using depth-guided dynamic programming based on spiral scanning
is proposed. The method automatically adjusts aggressiveness of the segmentation according
to the position of the voxels relative to the lesion center. Segmentation is more aggressive in the
upper part of the lesion (close to the transducer) than at the bottom (far away from the transducer)