A hybrid method towards automated nipple detection in 3D breast ultrasound images

Abstract

In clinical work-up of breast cancer, nipple position is an important marker to locate lesions. Moreover, it serves as an effective landmark to register a 3D automated breast ultrasound (ABUS) images to other imaging modalities, e.g., X-ray mammography, tomosynthesis or magnetic resonance imaging (MRI). However, the presence of speckle noises caused by the interference waves and variant imaging directions poses challenges to automatically identify nipple positions. In this work, a hybrid fully automatic method to detect nipple positions in ABUS images is presented. The method extends the multi-scale Laplacian-based method that we proposed previously, by integrating a specially designed Hessian-based method to locate the shadow area beneath the nipple and areola. Subsequently, the likelihood maps of nipple positions generated by both methods are combined to build a joint-likelihood map, where the final nipple position is extracted. To validate the efficiency and robustness, the extended hybrid method was tested on 926 ABUS images, resulting in a distance error of 7.08±10.96 mm (mean±standard deviation).

Automated quality assessment in three-dimensional breast ultrasound images

“…It is, however, unclear how to handle these cases in clinical practice. One possibility is to use an automatic nipple detection algorithm 16 to determine whether the nipple is visible or not. Another option is to find an agreement with the technicians on how to handle the cases where they do not see the nipple in the image.…”

“…The ABUS images were prepared for feature extraction in several preprocessing steps. First, a two-dimensional (2-D) coronal breast mask was computed similarly to the approach proposed by Wang et al 16 Therefore, a coronal mean projection of a stack of 120 slices close to the skin was performed. However, the top 50 slices from the skin were excluded from the breast mask computation to avoid responses from skin tissue.…”