A two-stage data-model driven pancreas segmentation strategy embedding directional information of the boundary...

In this paper, we propose a two-stage data-model driven pancreas segmentation method that combines a 3D convolution neural network with adaptive pointwise parametric hybrid variational model embedding the directional and magnitude information of the boundary intensity gradient. Firstly, nnU-net is used to segment the entire abdominal CT image with the aim of obtaining the region of the interest of pancreas. Secondly, an adaptive pointwise parametric variational model with a new edge term containing the directional and magnitude information of the boundary intensity gradient is used to refine the predicted results from CNN. Although CNN is good at extracting texture information, it does not capture weak boundary information very well. In order to well acquire more weak boundary information of the pancreas, we utilize not only the magnitude of the gradient, but also the directional information of the boundary intensity gradient to obtain more accurate results in the new edge term. In addition, the probability value for each pixel obtained by calculating the softmax function is exploited twice. Actually, it is applied firstly to generate the binary map as the initial contour of the variational model and then to design the adaptive pointwise weight parameters of internal and external area terms of the variational model rather than constants. It not only eliminates the trouble of manual parameter adjustment, but also, most importantly, provides a more accurate pointwise evolutionary trend of the level set contour, i.e. determine the tendency of the level set contour to pointwisely contract inward or expand outward. Our method is evaluated on three public datasets and outperformed the state-of-the-art pancreas segmentation methods. Accurate pancreatic segmentation allows for more reliable quantitative analysis of local morphological changes in the pancreas, which can assist in early diagnosis and treatment planning.