Evaluation of a Novel Method to Segment the Pectoral Muscle Surface in Automated Whole Breast Ultrasound

A. Gubern-Mérida, T. Tan, J. van Zelst, R. Mann, B. Platel and N. Karssemeijer

Annual Meeting of the Radiological Society of North America 2015.

PURPOSE Segmentation of anatomical structures in automated 3D breast ultrasound (ABUS) is required for development of computer-aided detection (CAD) and other techniques to make clinical workflow more efficient, such as automatic linking of findings between different ABUS views and multimodal registration. We propose a novel method to segment the anterior pectoral surface in ABUS images. METHOD AND MATERIALS We randomly collected 74 ABUS (25 Anterior-Posterior, 15 MEDial, 31 LATeral and 3 SUPerior views) volumes obtained in routine clinical care at two medical centers using the S2000 automated 3D breast ultrasound system (Siemens, Erlangen, Germany). Manual pectoral muscle delineations of the anterior surface were provided by a trained researcher. We developed an algorithm to segment the pectoral muscle surface in ABUS volumes: First, the chest wall is segmented using a previously validated software that models the chest wall as a cylinder. Thereafter, the chest wall surface is used to perform a cylindrical transformation on the ABUS volume. By applying this transformation, the chest wall and the pectoral muscle are straightened and shape variability of the pectoral muscle across volumes can be encoded in a probabilistic atlas. In the last step, gradient and atlas information are used to guide the pectoral muscle surface segmentation in a dynamic programming approach. The algorithm was applied to the 74 ABUS volumes of the study dataset following a leave-one-out strategy. Distance (mean+-stdev) between manual and automated pectoral muscle surfaces was used as evaluation measure. RESULTS The presented approach achieved a mean surface distance error of 3.47+-3.03 mm, compared to the manual annotations. The surface distance error for AP, LAT, MED and SUP view volumes was 2.61+-4.15, 3.78+-4.15, 4.17+-2.37 and 3.78+-1.02 mm, respectively. CONCLUSION Automated pectoral muscle segmentation is challenging due to high variation in pectoral muscle anatomy. The proposed method shows promising results on segmenting the pectoral muscle surface. CLINICAL RELEVANCE/APPLICATION ABUS is a promising modality for screening but reading is time consuming for radiologists. Availability of supporting tools such as computer-aided detection may expedite introduction of ABUS in practice.