Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

By Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo. Annual Conference on Medical Image Understanding and Analysis, 2017

785 citations recorded by the source corpus.

A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/e0067a9c3f30aab46d9e6f8844dc36e83c85869f

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Citations in the corpus

Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, background): “2 The accuracy of CNN classiﬁers is signiﬁcantly higher compared with traditional approaches, making them suitable for radiologists in real-world clinical practice [37], [38].”
Brain tumor detection and classification using machine learning: a comprehensive survey (2021, methodology): “The classiﬁcation of brain tumors has been done using two different types of networks, i.e., visual attention network and CNN are utilized for classiﬁcation of different types of brain tumor i.e., glioma, pituitary I and meningioma [234].”
TIDE: A General Toolbox for Identifying Object Detection Errors (2020, background): “Object detection and instance segmentation are fundamental tasks in computer vision, with applications ranging from self-driving cars [6] to tumor detection [9].”
Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network (2018, methodology): “Of note, adopting the FCN for image semantic segmentation is faster than the traditional method based on patch-wise [22, 36]; despite computational e ﬃ ciency, tests reported in the literature were done using slightly di ﬀ erent computing platforms.”
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018, background): “Our main contributions are: • We propose a U-Net architecture [47], [48] with skip connections for the generator network; • A reﬁnement learning approach is designed to stabilise the training of GAN for fast convergence and less parameter tuning; • The adversarial loss is coupled with a novel…”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, neutral): “While several deep learning based approaches have been proposed for speci ﬁ c tasks, such as tumor segmentation (Rani and Vashisth; Dong et al., 2017; Arunachalam and Savarimuthu, 2017; Havaei et al., 2017; Amin et al., 2018; Brainless Glioma , 2018), brain lesion segmentation (Kamnitsas et al., 201”
Deep learning based brain tumor segmentation: a survey (2020, neutral): “CNN Single-Path [26], [27], [28], [29], [30], [31], [32] Multi-Path [33], [34], [35], [36], [37], [38], [39], [40], [41], [42] FCN Vanilla FCN [43], [44], [28], [45], [46], [47], [48], [49], [50] U/V-Net Based [51], [52], [53], [54], [55], [56], [57], [58], [59], [60] Cascaded CNN [61], [62], [63], ”
Medical Image Segmentation Using Deep Learning: A Survey (2020, methodology): “[81] randomly used the brightness enhancement function in 3D MR images to”
Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors (2021, background): “A classic FCN architecture which attained widespread use across various applications (Akeret et al., 2017; Dong et al., 2017) is the U-net (Ronneberger et al., 2015), where upsampling layers are augmented with feature maps from the down-sampling path at the corresponding resolution, to provide more…”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, methodology): “U-net has been used extensively in this regard for the segmentation of brain structures as many different U-net models have been applied on MR images for brain tumor diagnosis [18], [19], [44], [45], [59], [81], [87], [92], [111]–[125].”