Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI

Abstract

Dynamic MRI has been widely used to track the motion of the tongue and measure its internal deformation during speech and swallowing. Accurate segmentation of the tongue is a prerequisite step to define the target boundary and constrain the tracking to tissue points within the tongue. Segmentation of 2D slices or 3D volumes is challenging because of the large number of slices and time frames involved in the segmentation, as well as the incorporation of numerous local deformations that occur throughout the tongue during motion. In this paper, we propose a semi-automatic approach to segment 3D dynamic MRI of the tongue. The algorithm steps include seeding a few slices at one time frame, propagating seeds to the same slices at different time frames using deformable registration, and random walker segmentation based on these seed positions. This method was validated on the tongue of five normal subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of a total of 130 volumes showed an average dice similarity coefficient (DSC) score of 0.92 with less segmented volume variability between time frames than in manual segmentations.

From CT scanning to 3-D printing technology for the preoperative planning in laparoscopic splenectomy

“…However, as the cost of 3-D printing technology declines and semiautomatic protocols are developed for the reconstruction of 3-D virtual models [24,25], it is easy to imagine that additive manufacturing production of anatomical objects may become affordable and worth doing for many scenarios in general surgery. Although the 3-D printing of anatomical objects is currently an undeveloped field, we believe that it might soon become a relevant topic in medicine, opening new areas of research in surgical education.…”

Minimally Interactive MRI Segmentation for Subject-Specific Modelling of the Tongue

“…The mean of the inter-subject standard deviation (STD) also dropped from 1.87 ± 0.60 to 0.60 ±0. 16. Table 1-bottom shows the volume overlap for group 2 (females).…”

“…Although the aforementioned method proposed by Lee et al [16], is very useful for motion analysis of the tongue, the different nature of cine and static MRI makes the method inefficient for accurate segmentation of a single high resolution static MRI volume. Figure 1 compares the two datasets.…”

“…Quite recently, Lee et al proposed a semi-automatic workflow for 3D segmentation of the tongue in dynamic MRI [16]. Their dataset consists of three orthogonal stacks of 2D MR slices, captured in 26 time frames for two English speaking subjects.…”

A spatio-temporal atlas and statistical model of the tongue during speech from cine-MRI

“…In addition, tagged-MRI (Parthasarathy et al 2007) allows us to observe internal tissue point motion, thereby detailing our understanding of the role of internal muscles during speech. Further, recent advances in various MRI methods have accelerated new advances in image and motion analyses such as segmentation of the tongue (Harandi et al 2014; Lee et al 2014) and internal muscles (Ibragimov et al 2015), internal motion tracking (Parthasarathy et al 2007), motion clustering (Woo et al 2014), and registration (Woo et al 2015c; Kim et al 2014) for various applications.…”