Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model

T. Tan, A. Gubern-Mérida, C. Borelli, R. Manniesing, J. van Zelst, L. Wang, W. Zhang, B. Platel, R. Mann and N. Karssemeijer

Medical Physics 2016;43(7):4074-4084.

DOI PMID Cited by ~15

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)