Abstract
Maxillary sinus segmentation plays an important role in the choice of therapeutic strategies for nasal disease and treatment monitoring. Difficulties in traditional approaches deal with extremely heterogeneous intensity caused by lesions, abnormal anatomy structures, and blurring boundaries of cavity. 2D and 3D deep convolutional neural networks have grown popular in medical image segmentation due to utilization of large labeled datasets to learn discriminative features. However, for 3D segmentation in medical images, 2D networks are not competent in extracting more significant spacial features, and 3D ones suffer from unbearable burden of computation, which results in great challenges to maxillary sinus segmentation. In this paper, we propose a deep neural network with an end-to-end manner to generalize a fully automatic 3D segmentation. At first, our proposed model serves a symmetrical encoder-decoder architecture for multitask of bounding box estimation and in-region 3D segmentation, which cannot reduce excessive computation requirements but eliminate false positives remarkably, promoting 3D segmentation applied in 3D convolutional neural networks. In addition, an overestimation strategy is presented to avoid overfitting phenomena in conventional multitask networks. Meanwhile, we introduce residual dense blocks to increase the depth of the proposed network and attention excitation mechanism to improve the performance of bounding box estimation, both of which bring little influence to computation cost. Especially, the structure of multilevel feature fusion in the pyramid network strengthens the ability of identification to global and local discriminative features in foreground and background achieving more advanced segmentation results. At last, to address problems of blurring boundary and class imbalance in medical images, a hybrid loss function is designed for multiple tasks. To illustrate the strength of our proposed model, we evaluated it against the state-of-the-art methods. Our model performed better significantly with an average Dice 0.947±0.031, VOE 10.23±5.29, and ASD 2.86±2.11, respectively, which denotes a promising technique with strong robust in practice.
Highlights
Maxillary sinus is an important part of the body which has multiple functions including olfaction, filtering, heating, and humidifying the inhaled air
In Mask R-CNN, the RoIAlign module for localization of region of interest (RoI) runs bilinear interpolation to resample feature tensors in the anchors to fixed dimensions. Such mechanism results in losing features of details, giving challenges to medical images with the low-level resolution. To address these issues and inspired by the Mask R-CNN with ResNet-FPN [34] and residual attention network (RAN) [47], we propose a novel multitask framework for segmentation with 3D bounding box estimation, named as 3D bounding box estimation feature pyramid network (BEFNet), which is designed to effectively extract 3D volumetric maxillary sinus from CT scans in an end-to-end manner
Sufficient ablation studies on collected 50 CT scans demonstrated the superiority of our proposed model with the following main contributions: (1) We propose a deep neural network with multitask of 3D bounding box estimation and in-region segmentation branches
Summary
Maxillary sinus is an important part of the body which has multiple functions including olfaction, filtering, heating, and humidifying the inhaled air. People who suffer from nasal function impairment may have a reduced quality of life [1]. In the last few years, functional endoscopic sinus surgery (FESS) has been established as the state-of-the-art technique for the treatment of endonasal pathologies. To exactly define the workspace, the knowledge about the anatomical structure of maxillary sinus is required. Manual segmentation costs about 900 minutes for one patient’s CT scans which become infeasible for daily practice [2]. Automatic segmentation approaches with high accuracy should be imperative. The high rate of structure variations exists in maxillary sinus like location, size, and shape.
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