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

Dong, Hao; Yang, Guang; Liu, Fangde; Mo, Yuanhan; Guo, Yike

doi:10.1007/978-3-319-60964-5_44

Cited by 640 publications

(406 citation statements)

References 25 publications

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“…In other words, the algorithm queries are directed only toward areas that are hard to segment by the algorithm with the goal of reducing time‐consuming slice‐by‐slice user verification. Recent works on segmentation of brain tumors allow automatic detection of tumors and achieve closer‐to‐expert manual segmentations than those obtained with the semiautomatic method used here . Clearly, more advanced and fully automated deep learning methods could lead to more accurate tumor segmentations and speed up the workflow for automated radiomic analysis.…”

Section: Discussionmentioning

confidence: 95%

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI‐radiomic features

Tixier

Young

et al. 2019

Medical Physics

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Purpose The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features. Method Fluid‐attenuated inversion recovery (FLAIR) and contrast‐enhanced T1‐weighted (T1WICE) MRI of 90 patients diagnosed with glioblastoma were segmented using a semiautomatic algorithm and an interactive segmentation with two different raters. We analyzed the robustness of 108 radiomic features from five categories (intensity histogram, gray‐level co‐occurrence matrix, gray‐level size‐zone matrix (GLSZM), edge maps, and shape) using intra‐class correlation coefficient (ICC) and Bland and Altman analysis. Results Our results show that both segmentation methods are reliable with ICC ≥ 0.96 and standard deviation (SD) of mean differences between the two raters (SDdiffs) ≤ 30%. Features computed from the histogram and co‐occurrence matrices were found to be the most robust (ICC ≥ 0.8 and SDdiffs ≤ 30% for most features in these groups). Features from GLSZM were shown to have mixed robustness. Edge, shape, and GLSZM features were the most impacted by the choice of segmentation method with the interactive method resulting in more robust features than the semiautomatic method. Finally, features computed from T1WICE and FLAIR images were found to have similar robustness when computed with the interactive segmentation method. Conclusion Semiautomatic and interactive segmentation methods using two raters are both reliable. The interactive method produced more robust features than the semiautomatic method. We also found that the robustness of radiomic features varied by categories. Therefore, this study could help motivate segmentation methods and feature selection in MRI radiomic studies.

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Section: Discussionmentioning

confidence: 95%

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI‐radiomic features

Tixier

Young

et al. 2019

Medical Physics

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“…The networks explored in this work are built on the UNet architecture, which has shown outstanding performance in various medical segmentation tasks . This network consists of a contracting and expanding path, the former collapsing an image down into a set of high‐level features and the latter using these features to construct a pixel‐wise segmentation mask.…”

Section: Methodsmentioning

confidence: 99%

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Dolz

Rony

et al. 2018

Medical Physics

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Purpose Precise segmentation of bladder walls and tumor regions is an essential step toward noninvasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC). However, the automatic delineation of bladder walls and tumor in magnetic resonance images (MRI) is a challenging task, due to important bladder shape variations, strong intensity inhomogeneity in urine, and very high variability across the population, particularly on tumors’ appearance. To tackle these issues, we propose to leverage the representation capacity of deep fully convolutional neural networks. Methods The proposed network includes dilated convolutions to increase the receptive field without incurring extra cost or degrading its performance. Furthermore, we introduce progressive dilations in each convolutional block, thereby enabling extensive receptive fields without the need for large dilation rates. The proposed network is evaluated on 3.0T T2‐weighted MRI scans from 60 pathologically confirmed patients with BC. Results Experiments show the proposed model to achieve a higher level of accuracy than state‐of‐the‐art methods, with a mean Dice similarity coefficient of 0.98, 0.84, and 0.69 for inner wall, outer wall, and tumor region segmentation, respectively. These results represent a strong agreement with reference contours and an increase in performance compared to existing methods. In addition, inference times are less than a second for a whole three‐dimensional (3D) volume, which is between two and three orders of magnitude faster than related state‐of‐the‐art methods for this application. Conclusion We showed that a CNN can yield precise segmentation of bladder walls and tumors in BC patients on MRI. The whole segmentation process is fully automatic and yields results similar to the reference standard, demonstrating the viability of deep learning models for the automatic multiregion segmentation of bladder cancer MRI images.

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“…• 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 content loss considering both pixel-wise mean square error (MSE) and perceptual loss defined by pretrained deep convolutional networks from the Visual Geometry Group at Oxford University (in short VGG networks [49]) to achieve better reconstruction details; • Frequency domain information of the CS-MRI has been incorporated as additional constraints for the data consistency, which is formed as an extra loss term; • We perform comprehensive experiments and compare our proposed models with both classic CS-MRI and newly developed deep learning based methods. Compared to the state-of-the-art CS-MRI methods, we can achieve high acceleration factors with superior results and faster processing time.…”

Section: Our Contributionsmentioning

confidence: 99%

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Yang

Dong

et al. 2018

IEEE Trans. Med. Imaging

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Abstract-Compressed Sensing Magnetic Resonance Imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging based fast MRI, which utilises multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training datasets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN) is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilise our U-Net based generator, which provides an endto-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared to these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

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

Cited by 640 publications

References 25 publications

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI‐radiomic features

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI‐radiomic features

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

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