Abstract
In this paper, we propose a novel method, an adaptive localizing region-based level set using convolutional neural network, for improving performance of maxillary sinus segmentation. The healthy sinus without lesion inside is easy for conventional algorithms. However, in practice, most of the cases are filled with lesions of great heterogeneity which lead to lower accuracy. Therefore, we provide a strategy to avoid active contour from being trapped into a nontarget area. First, features of lesion and maxillary sinus are studied using a convolutional neural network (CNN) with two convolutional and three fully connected layers in architecture. In addition, outputs of CNN are devised to evaluate possibilities of zero level set location close to lesion or not. Finally, the method estimates stable points on the contour by an interactive process. If it locates in the lesion, the point needs to be paid a certain speed compensation based on the value of possibility via CNN, assisting itself to escape from the local minima. If not, the point preserves current status till convergence. Capabilities of our method have been demonstrated on a dataset of 200 CT images with possible lesions. To illustrate the strength of our method, we evaluated it against state-of-the-art methods, FLS and CRF-FCN. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better compared with currently available methods and obtained a significant Dice improvement, 0.25 than FLS and 0.12 than CRF-FCN, respectively, on an average.
Highlights
Nasal diseases are growing common for individuals with serious impacts on daily life
All of them have the same 512 × 512 in-plane resolution but with a different number of axial slices. e spacing between pixels along ZYX axes of the acquired dataset falls within from 0.5 × 0.35 × 0.35 mm to 0.625 × 0.39 × 0.39 mm
Most of them are based on gray gradient which result in the active contour falling in local minima of lesion edge
Summary
Nasal diseases are growing common for individuals with serious impacts on daily life. For chronic one, functional endonasal sinus surgery (FESS) may be the only solution of relief. It is found that a lot of risks may appear in surgery due to great variations of nasal anatomy, where optical nerves and carotids have higher possibility to be affected [4]. Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of diseases, and quantitative imaging can reveal clues about lesion characteristic and anatomical structure. A real-time surgical navigation has joined in FESS [6]. To make FESS safer, surgeons use navigation systems that register a patient to his/her CT scan and track the position of tools inside the patient [7]
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