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首页> 外文期刊>BMC Medical Informatics and Decision Making >Learning rich features with hybrid loss for brain tumor segmentation
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Learning rich features with hybrid loss for brain tumor segmentation

机译：学习具有脑肿瘤细分的杂种损失的丰富特征

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Accurately segment the tumor region of MRI images is important for brain tumor diagnosis and radiotherapy planning. At present, manual segmentation is wildly adopted in clinical and there is a strong need for an automatic and objective system to alleviate the workload of radiologists. We propose a parallel multi-scale feature fusing architecture to generate rich feature representation for accurate brain tumor segmentation. It comprises two parts: (1) Feature Extraction Network (FEN) for brain tumor feature extraction at different levels and (2) Multi-scale Feature Fusing Network (MSFFN) for merge all different scale features in a parallel manner. In addition, we use two hybrid loss functions to optimize the proposed network for the class imbalance issue. We validate our method on BRATS 2015, with 0.86, 0.73 and 0.61 in Dice for the three tumor regions (complete, core and enhancing), and the model parameter size is only 6.3?MB. Without any post-processing operations, our method still outperforms published state-of-the-arts methods on the segmentation results of complete tumor regions and obtains competitive performance in another two regions. The proposed parallel structure can effectively fuse multi-level features to generate rich feature representation for high-resolution results. Moreover, the hybrid loss functions can alleviate the class imbalance issue and guide the training process. The proposed method can be used in other medical segmentation tasks.
机译：准确地段MRI图像的肿瘤区域对于脑肿瘤诊断和放射治疗计划是重要的。目前，手动分割在临床中野外采用，并且有强烈需要自动和客观的系统来缓解放射科医师的工作量。我们提出了一种平行的多尺度特征融合架构，以产生丰富的特征表示，以获得精确的脑肿瘤细分。它包括两个部分：（1）用于不同水平的脑肿瘤特征提取的特征提取网络（FEN）和（2）多尺度特征定影网络（MSFFN）以并行方式合并所有不同的尺度特征。此外，我们使用两个混合丢失函数来优化所提出的网络，以获取类别不平衡问题。我们验证了我们在Brats 2015上的方法，三个肿瘤区域（完整，核心和增强）的骰子中的0.86,0.73和0.61，而模型参数大小仅为6.3？MB。如果没有任何后处理操作，我们的方法仍然优于完整肿瘤地区的分段结果上发表的最先进方法，并在另外两个地区获得竞争性能。所提出的并行结构可以有效地熔化多级功能以产生高分辨率结果的丰富特征表示。此外，混合丢失功能可以缓解类别不平衡问题并指导培训过程。所提出的方法可用于其他医学分段任务。

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