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Automated Skin Segmentation in Ultrasonic Evaluation of Skin Toxicity in Breast Cancer Radiotherapy

1Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA.
2Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Publication Date:
Ultrasound Med Biol
Volume Number:
Issue Number:
Ultrasound Med Biol. 2013 Nov;39(11):2166-75.
PubMed ID:
Breast cancer radiotherapy, Breast ultrasound, Radiation toxicity, Skin segmentation
Appears in Collections:
K01 CA114313/CA/NCI NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Gao Y., Tannenbaum A., Chen H., Torres M., Yoshida E., Yang X., Wang Y., Curran W., Liu T. Automated Skin Segmentation in Ultrasonic Evaluation of Skin Toxicity in Breast Cancer Radiotherapy. Ultrasound Med Biol. 2013 Nov;39(11):2166-75. PMID: 23993172. PMCID: PMC3913784.
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Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and -3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic.

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