An analysis of the segmentation threshold used in axial-shear strain elastography

Axial-shear strain elastography was introduced recently to image the tumor-host tissue boundary bonding characteristics. The image depicting the axial-shear strain distribution in a tissue under axial compression was termed as an axial-shear strain elastogram (ASSE). It has been demonstrated through simulation, tissue-mimicking phantom experiments, and retrospective analysis of in vivo breast lesion data that metrics quantifying the pattern of axial-shear strain distribution on ASSE can be used as features for identifying the lesion boundary condition as loosely-bonded or firmly-bonded. Consequently, features from ASSE have been shown to have potential in non-invasive breast lesion classification into benign versus malignant. Although there appears to be a broad concurrence in the results reported by different groups, important details pertaining to the appropriate segmentation threshold needed for - (1) displaying the ASSE as a color-overlay on top of corresponding Axial Strain Elastogram (ASE) and/or sonogram for feature visualization and (2) ASSE feature extraction are not yet fully addressed. In this study, we utilize ASSE from tissue mimicking phantom (with loosely-bonded and firmly-bonded inclusions) experiments and freehand - acquired in vivo breast lesion data (7 benign and 9 malignant) to analyze the effect of segmentation threshold on ASSE feature value, specifically, the "fill-in" feature that was introduced recently. We varied the segmentation threshold from 20% to 70% (of the maximum ASSE value) for each frame of the acquisition cine-loop of every data and computed the number of ASSE pixels within the lesion that was greater than or equal to this threshold value. If at least 40% of the pixels within the lesion area crossed this segmentation threshold, the ASSE frame was considered to demonstrate a "fill-in" that would indicate a loosely-bonded lesion boundary condition (suggestive of a benign lesion). Otherwise, the ASSE frame was considered not to demonstrate a "fill-in" indicating a firmly-bonded lesion boundary condition (suggestive of a malignant lesion). The results demonstrate that in the case of in vivo breast lesion data the appropriate range for the segmentation threshold value seems to be 40-60%. It was noted that for a segmentation threshold within this range (for example, at 50%) all of the analyzed breast lesion cases can be correctly classified into benign and malignant, based on the percentage number of frames within the acquisition cine-loop that demonstrate a "fill-in".