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摘要
Objective: Accurate diagnosis and early prognosis of breast cancer can increase the survival rates of breast cancer patients. In clinical applications, the process of breast cancer treatment often contains neoadjuvant chemotherapy (NAC) which attempts to reduce tumor size and increase the chance of breast-conserving surgery. However, some patients do not respond positively to NAC and do not show a pathologically complete response. For these patients, NAC is time consuming and highly risky. Therefore, exploring an efficient method for precisely predicting NAC response is essential. A potential scheme is to use medical imaging techniques, such as magnetic resonance imaging in building a computer-assisted diagnosis (CAD) system for predicting NAC response. Most existing CAD methods focus on tumor features, which are highly related to region of interest (ROI) segmentations. At present, breast tumor is segmented manually, and this method cannot satisfy real-time and accurate segmentation requirements. Automatic breast tumor segmentation is a potential way to deal with such issue. Although numerous works about breast tumor segmentation have been proposed and some of them have achieved good results, they mainly focus on the segmentation of single-center datasets. How to improve the generalization ability of a model and ensure its good performance in multicenter datasets is still presents great challenge. To address this problem, we proposed a semantic Laplacian pyramid network (SLAPNet) for segmenting breast tumor with multicenter datasets. Method: SLAPNet is composed of Gaussian and semantic pyramids. The Gaussian pyramid is used for creating multilevel inputs to enable the model to notice not only global image features, such as shape and gray-level distribution, but also local image features, such as edges and textures. It is implemented by smoothing and downsampling input images with Gaussian filters, which can denoise the images and blur details. Thus, the characteristics of large structures in the images can be highlighted. By combining these multiscale inputs, SLAPNet is more robust and generalized, so it can handle irregular objects. The semantic pyramid is produced first after UNet extracts deep semantic features with multilevel inputs and then connects adjacent layers to transfer deep semantic features to different layers. This strategy fuses multi-semantic-level and multilevel features to improve model performance. To reduce the influence of class imbalance, we selected Dice loss as our loss function. To validate the superiority of the proposed method, we trained SLAPNet and other state-of-the-art models with multicenter datasets. Finally, the accuracy (ACC), specificity, sensitivity (SEN), Dice similarity coefficient (DSC), precision, and Jaccard coefficient were used in quantitatively analyzing the segmentation results. Result: Compared with Attention UNet, DeeplabV3, fully convolutional network(FCN), pyramid scene parsing network(PSPNet), UNet, UNet3+, multiscale dual attention network(MSDNet), and pyramid convolutional network(PyConvUNet), the DSC of our model was the highest, with a value of 0.83 when the model was tested on the dataset acquired from Harbin Medical University Cancer Hospital and a value of 0.77 when the model was tested on the public I-SPY 1(investigation of aerial studies to predict your therapeutic response with imaging and moLecular analysis 1) dataset, increasing by at least 1.3%. The visualization results illustrated that SLAPNet produced a small amount of misclassification and omission in the marginal regions and the segmented edge was better than the segmented edges of the other models. The visualization results of error maps indicated that SLAPNet outperformed other models in breast tumor segmentation. Finally, to further validate the stability of the proposed model, we provided the boxplots of the evaluation metrics, which demonstrated that the DSC, Jaccard coefficient, SEN, and ACC of the proposed model were higher than those of the other models and the three quartiles of the proposed model were closer, indicating that SLAPNet was more stable for multicenter breast tumor segmentation. Conclusion: The semantic Laplacian pyramid network proposed in this paper extracted deep semantic features from multilevel inputs and then fused multiscale semantic deep features. This structure guaranteed the high expressive ability of the deep features. We were able to capture more expressive features related to image details by combining multiscale semantic features. Therefore, our proposed model can better distinguish edges and texture features in tumors. The results demonstrated that the pyramid model showed the best performance in multicenter breast cancer tumor segmentation. ? 2021, Editorial Office of Journal of Image and Graphics. All right reserved.
