Sinus floor elevation with a lateral window approach requires bone graft(BG)to ensure sufficient bone mass,and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.However,the B...Sinus floor elevation with a lateral window approach requires bone graft(BG)to ensure sufficient bone mass,and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.However,the BG region from cone-beam computed tomography(CBCT)images is connected to the margin of the maxillary sinus,and its boundary is blurred.Common segmentation methods are usually performed manually by experienced doctors,and are complicated by challenges such as low efficiency and low precision.In this study,an auto-segmentation approach was applied to the BG region within the maxillary sinus based on an atrous spatial pyramid convolution(ASPC)network.The ASPC module was adopted using residual connections to compose multiple atrous convolutions,which could extract more features on multiple scales.Subsequently,a segmentation network of the BG region with multiple ASPC modules was established,which effectively improved the segmentation performance.Although the training data were insufficient,our networks still achieved good auto-segmentation results,with a dice coefficient(Dice)of 87.13%,an Intersection over Union(Iou)of 78.01%,and a sensitivity of 95.02%.Compared with other methods,our method achieved a better segmentation effect,and effectively reduced the misjudgement of segmentation.Our method can thus be used to implement automatic segmentation of the BG region and improve doctors’work efficiency,which is of great importance for developing preliminary studies on the measurement of postoperative BG within the maxillary sinus.展开更多
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
基金the National Key Research and Development Program of China(No.2017YFB1302900)the National Natural Science Foundation of China(Nos.81971709,M-0019,and 82011530141)+2 种基金the Foundation of Science and Technology Commission of Shanghai Municipality(Nos.19510712200,and 20490740700)the Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research(Nos.ZH2018ZDA15,YG2019ZDA06,and ZH2018QNA23)the 2020 Key Research Project of Xiamen Municipal Government(No.3502Z20201030)。
文摘Sinus floor elevation with a lateral window approach requires bone graft(BG)to ensure sufficient bone mass,and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.However,the BG region from cone-beam computed tomography(CBCT)images is connected to the margin of the maxillary sinus,and its boundary is blurred.Common segmentation methods are usually performed manually by experienced doctors,and are complicated by challenges such as low efficiency and low precision.In this study,an auto-segmentation approach was applied to the BG region within the maxillary sinus based on an atrous spatial pyramid convolution(ASPC)network.The ASPC module was adopted using residual connections to compose multiple atrous convolutions,which could extract more features on multiple scales.Subsequently,a segmentation network of the BG region with multiple ASPC modules was established,which effectively improved the segmentation performance.Although the training data were insufficient,our networks still achieved good auto-segmentation results,with a dice coefficient(Dice)of 87.13%,an Intersection over Union(Iou)of 78.01%,and a sensitivity of 95.02%.Compared with other methods,our method achieved a better segmentation effect,and effectively reduced the misjudgement of segmentation.Our method can thus be used to implement automatic segmentation of the BG region and improve doctors’work efficiency,which is of great importance for developing preliminary studies on the measurement of postoperative BG within the maxillary sinus.
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
