  Previous studies by our group have shown that three-dimensional
high-frequency quantitative ultrasound methods have the potential to
differentiate metastatic lymph nodes from cancer-free lymph nodes dissected
from human cancer patients. To successfully perform these methods inside the
lymph node parenchyma, an automatic segmentation method is highly desired to
exclude the surrounding thin layer of fat from quantitative ultrasound
processing and accurately correct for ultrasound attenuation. In high-frequency
ultrasound images of lymph nodes, the intensity distribution of lymph node
parenchyma and fat varies spatially because of acoustic attenuation and
focusing effects. Thus, the intensity contrast between two object regions
(e.g., lymph node parenchyma and fat) is also spatially varying. In our
previous work, nested graph cut demonstrated its ability to simultaneously
segment lymph node parenchyma, fat, and the outer phosphate-buffered saline
bath even when some boundaries are lost because of acoustic attenuation and
focusing effects. This paper describes a novel approach called graph cut with
locally adaptive energy to further deal with spatially varying distributions of
lymph node parenchyma and fat caused by inhomogeneous acoustic attenuation. The
proposed method achieved Dice similarity coefficients of 0.937+-0.035 when
compared to expert manual segmentation on a representative dataset consisting
of 115 three-dimensional lymph node images obtained from colorectal cancer
patients.
