Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation.
Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation.
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DOI：
10.1109/jtehm.2022.3176737
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发表时间：
2022
影响因子：
3.4
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中科院分区：
文献类型：
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背景：利用磁共振图像对脑肿瘤进行检测和分割是医学领域具有挑战性和价值的课题。脑肿瘤的早期诊断和定位可以挽救生命，并为医生及时选择有效的治疗方案提供选择。深度学习方法因其在准确诊断、预后和医疗技术方面的能力、性能和潜力而吸引了医学成像领域的研究人员。方法和步骤：本文提出了一种利用深度神经网络（DNN）和数据增强策略在MR图像中分割二维脑肿瘤的新框架。所提出的方法（Znet）基于跳过连接、编码器-解码器架构和数据放大的思想，将相对较少数量的专家描述的肿瘤（例如，数百名低级别胶质瘤（LGG）患者）的内在亲和力传播到数千名合成病例。结果：我们的实验结果显示，骰子的平均相似系数很高（模型训练时的骰子= 0.96，独立测试数据集的骰子= 0.92）。其他评价指标也比较高，如像素精度= 0.996，F1得分= 0.81，Matthews相关系数，MCC = 0.81。测试数据集中dnn衍生的肿瘤掩模的结果和可视化显示了ZNet模型在MR图像中定位和自动分割脑肿瘤的能力。这种方法可以进一步推广到3D脑体积，其他病理，和广泛的图像模式。结论：我们可以证实深度学习方法和提出的Znet框架在MR图像中检测和分割肿瘤的能力。此外，在MR图像分类不平衡的情况下，像素精度评价可能不是语义分割的合适评价指标。这是因为在地面真实图像中占统治地位的阶级是背景。因此，在一些计算机视觉应用中，高像素精度值可能会产生误导。另一方面，其他评估指标，如dice和IoU（交集/联合），对于语义分割来说更实际。临床影响：人工智能（AI）在医学中的应用正在迅速发展，然而，在临床实践中缺乏可部署的技术。该研究展示了人工智能在医学成像中的应用实例，可以作为MR图像中肿瘤自动分割的工具。
Background: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies. Methods and procedures: This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), to many thousands of synthetic cases. Results: Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model’s capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities. Conclusion: We can confirm the ability of deep learning methods and the proposed Znet framework to detect and segment tumors in MR images. Furthermore, pixel accuracy evaluation may not be a suitable evaluation measure for semantic segmentation in case of class imbalance in MR images segmentation. This is because the dominant class in ground truth images is the background. Therefore, a high value of pixel accuracy can be misleading in some computer vision applications. On the other hand, alternative evaluation metrics, such as dice and IoU (Intersection over Union), are more factual for semantic segmentation. Clinical impact: Artificial intelligence (AI) applications in medicine are advancing swiftly, however, there is a lack of deployed techniques in clinical practice. This research demonstrates a practical example of AI applications in medical imaging, which can be deployed as a tool for auto-segmentation of tumors in MR images.