Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks

Cai, Jinzheng; Lü, Le; Zhang, Zizhao; Xing, Fuyong; Yang, Lin; Yin, Qian

doi:10.1007/978-3-319-46723-8_51

Cited by 82 publications

(72 citation statements)

References 7 publications

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“…The difficulty comes from the characteristics of the OARs segmentation task, such as the large variability of the shape and size across a different target structures and the poor contrast between some structures and their background. Deep neural networks have become the best choice for most image processing tasks and often outperform traditional methods with a large margin, including in many medical image segmentation applications (Cai et al, 2016;Cha et al, 2016;Hu et al, 2017;Milletari et al, 2017Milletari et al, , 2016Zhu et al, 2017). However, existing studies of applying deep neural networks in OARs segmentation of HaN CT images only achieve similar performance to traditional methods.…”

Section: Discussionmentioning

confidence: 99%

“…This challenge provides a unified evaluation framework for the research on OARs segmentation methods. Along with its big success in general image processing tasks, deep learning has also been widely used in medical image segmentation (Cai et al, 2016;Cha et al, 2016;Hu et al, 2017;Milletari et al, 2017Milletari et al, , 2016Zhu et al, 2017), including OARs segmentation in HaN CT images. Ibragimov and Xing (2017) applied 2D CNN in segmenting OARs of in house HaN CT images but only achieved slight improvement in the right submandibular gland and right optic nerve, and the performance on other OARs are similar to traditional methods.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, deep learning methods, especially the Convolutional Neural Networks (CNN), have demonstrated state-of-the-art performance in many image segmentation tasks, including segmentation of medical images (Cai et al, 2016;Cha et al, 2016;Hu et al, 2017;Milletari et al, 2017Milletari et al, , 2016Zhu et al, 2017). Several groups (Ibragimov and Xing, 2017;Ren et al, 2018) used deep learning method in OARs segmentation, but the improvement over traditional methods are not significant, and currently the superiority of deep learning approach in the OARs segmentation task is not fully revealed.…”

Section: Introductionmentioning

confidence: 99%

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Organ at Risk Segmentation in Head and Neck CT Images Using a Two-Stage Segmentation Framework Based on 3D U-Net

et al. 2019

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Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is challenging for both human and automatic algorithms because of the relatively large number of OARs to be segmented, the large variability of the size and morphology across different OARs, and the low contrast of between some OARs and the background. In this paper, we proposed a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two sub-tasks: locating a bounding box of the OAR and segment it from a small volume within the bounding box, and each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes each of the two sub-tasks much easier, so that they can be better completed. We evaluated the proposed method and compared it to state-ofthe-art methods by using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric 95HD, the proposed method ranked first in eight of all nine OARs and ranked second in the other OAR. In terms of the area-based metric DSC, the proposed method ranked first in six of the nine OARs and ranked second in the other three OARs with small difference with the first one.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Organ at Risk Segmentation in Head and Neck CT Images Using a Two-Stage Segmentation Framework Based on 3D U-Net

et al. 2019

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“…Deep learning is one popular kind of artificial intelligence algorithm. It has been widely applied in many fields and has achieved many remarkable results (Cai et al, 2016;Kim et al, 2017;Liu et al, 2017Liu et al, , 2018Sharma et al, 2017;Zhang and Zhou, 2018). Recently, studies of deep learning-based automatic sleep stage classification have also frequently been published (Fiorillo et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points

Yang

Sun

et al. 2020

Front. Neurosci.

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Sleep stage classification is an open challenge in the field of sleep research. Considering the relatively small size of datasets used by previous studies, in this paper we used the Sleep Heart Health Study dataset from the National Sleep Research Resource database. A long short-term memory (LSTM) network using a time-frequency spectra of several consecutive 30 s time points as an input was used to perform the sleep stage classification. Four classical convolutional neural networks (CNNs) using a time-frequency spectra of a single 30 s time point as an input were used for comparison. Results showed that, when considering the temporal information within the time-frequency spectrum of a single 30 s time point, the LSTM network had a better classification performance than the CNNs. Moreover, when additional temporal information was taken into consideration, the classification performance of the LSTM network gradually increased. It reached its peak when temporal information from three consecutive 30 s time points was considered, with a classification accuracy of 87.4% and a Cohen's Kappa coefficient of 0.8216. Compared with CNNs, our results indicate that for sleep stage classification, the temporal information within the data or the features extracted from the data should be considered. LSTM networks take this temporal information into account, and thus, may be more suitable for sleep stage classification.

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“…Further improvements in MRI technology might open the possibility, not only to detect tumours (PI‐RADS), but also to visualize the individual patient's nerve concourses (Moving Image – Fig. ) . The best technological option currently seems to be a fusing of this type of imaging so that real‐time visualization can be brought into the surgical field.…”

Section: Discussionmentioning

confidence: 99%

Extended periprostatic nerve distributions on the prostate surface confirmed using diffusion tensor imaging

et al. 2019

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Different types of nerve fibers demonstrate distribution variations. When linked to possible functional aspects of the different nerve types, this morphologic evidence may be of importance to further protect function after radical prostatectomy. To our knowledge this is the first time that DTI confirmed reported histological findings in nerve sparing radical prostatectomies. DTI could be used as an important tool to correlate nerves-to-tumor for better pre-operative planning and incorporate imaging into the treatment. This article is protected by copyright. All rights reserved.

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Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks

Cited by 82 publications

References 7 publications

Organ at Risk Segmentation in Head and Neck CT Images Using a Two-Stage Segmentation Framework Based on 3D U-Net

Organ at Risk Segmentation in Head and Neck CT Images Using a Two-Stage Segmentation Framework Based on 3D U-Net

Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points

Extended periprostatic nerve distributions on the prostate surface confirmed using diffusion tensor imaging

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