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Rethinking dice loss for medical image segmentation

BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2020 utilizes multi-institutional pre-operative MRI scans and primarily focuses on the segmentation (Task 1) of intrinsically heterogeneous (in appearance, shape, and.

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1. Introduction. In clinical practice, medical image analysis [] can provide physicians with digital and quantified medical information to help them make objective and accurate diagnoses.Medical image segmentation is important for medical image analysis and can be used for image-guided interventions, radiotherapy, or improved radiological diagnosis.

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Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image features by which to classify data. Convolutional neural networks (CNNs), the core of deep learning methods for.

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U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention, Vol. 9651, 234–241. Google Scholar; Ibtehaz M. Nabil and Sohel Rahman. 2020. MultiResUNet: Rethinking the U-Net architecture for multimodel biomedical image segmentation. Neur. Netw. (2020), 74–87. Google Scholar.

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In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images. This model consists of an encoder, a segmentation decoder, and a context residual decoder. We design the context residual module and use it to bridge both decoders at each scale. Each context residual module contains both.

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