Accurate detection and pixel-wise classification of brain tumors in Magnetic Resonance Imaging (MRI) scans are vital for their diagnosis, prognosis study and treatment planning. Manual segmentation of tumors from MRI is highly subjective and tedious. With recent advances in deep learning, automatic brain tumor segmentation is an emerging research direction in the medical imaging domain. We present a study to improve the automatic segmentation process by introducing size variability in the Convolutional Neural Network (CNN). For pixel-wise classification of tumorous slices convolutional neural network-based encoder-decoder UNET model is referred. A multi-inception-UNET model is proposed to improve scalability of the UNET model. Extensive experiments have been performed using the Brain Tumor Segmentation Challenge (BRATS) datasets to establish the validity of our proposed model. Experimental results show that our proposed method achieved the best results on BraTS 2015, 2017 and 2019 datasets for complete tumor, core tumor and enhancing tumor regions respectively.

中文翻译：

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使用多起始UNET的端到端脑肿瘤分割系统

磁共振成像 (MRI) 扫描中脑肿瘤的准确检测和像素级分类对其诊断、预后研究和治疗计划至关重要。从 MRI 手动分割肿瘤是非常主观和乏味的。随着深度学习的最新进展，自动脑肿瘤分割是医学成像领域的一个新兴研究方向。我们提出了一项研究，通过在卷积神经网络 (CNN) 中引入大小可变性来改进自动分割过程。对于肿瘤切片的像素级分类，参考了基于卷积神经网络的编码器-解码器 UNET 模型。提出了一种多初始 UNET 模型来提高 UNET 模型的可扩展性。已经使用脑肿瘤分割挑战 (BRATS) 数据集进行了大量实验，以建立我们提出的模型的有效性。实验结果表明，我们提出的方法分别在完整肿瘤、核心肿瘤和增强肿瘤区域的 BraTS 2015、2017 和 2019 数据集上取得了最佳结果。