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

Cai, Jinzheng; Lü, Le; Xie, Yuanpu; Xing, Fuyong; Yang, Lin

doi:10.1007/978-3-319-66179-7_77

Cited by 41 publications

(39 citation statements)

References 17 publications

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“…In their work, they use 2D CNNs to encode the features of 3D CT volumes. Although it has been shown that for some segmentation tasks, applying 2D CNNs to 3D data can give reasonably well results as the segmentation itself can sometimes be addressed slice by slice which favors 2D operations [21], [22]. However, as denoted in [23] and also demonstrated in this work, 2D CNNs do not work well in detection problems as they cannot capture the 3D spatial information that is critical to the detection of the target object.…”

Section: Introductionmentioning

confidence: 78%

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Liao¹,

Mesfin²,

Luo³

2018

IEEE Trans. Med. Imaging

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Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that: 1) one should make use of the short-range contextual information, such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; and 2) due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order, which provides the important long-range contextual information to further calibrate the results. We propose a robust and efficient vertebrae identification and localization system that can inherently learn to incorporate both the short- and long-range contextual information in a supervised manner. To this end, we develop a multi-task 3-D fully convolutional neural network to effectively extract the short-range contextual information around the target vertebrae. For the long-range contextual information, we propose a multi-task bidirectional recurrent neural network to encode the spatial and contextual information among the vertebrae of the visible spine column. We demonstrate the effectiveness of the proposed approach on a challenging data set, and the experimental results show that our approach outperforms the state-of-the-art methods by a significant margin.

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Section: Introductionmentioning

confidence: 78%

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Liao¹,

Mesfin²,

Luo³

2018

IEEE Trans. Med. Imaging

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“…However, CRF does reduce the standard deviation of Dice in all experiments, demonstrating its regularization capability in reducing inference variance. Cai et al (2016) use CRF to fuse mask and boundary predictions, which are separate branches off the same backbone during training, as a cascaded task that post-processes pancreas segmentation of MR Images. In this work, CRF still operates on neighbouring pixels in a feature space spanned by hand-crafted image features and the features learned by both segmentation branches.…”

Section: Locally Connected Crfmentioning

confidence: 99%

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Tajbakhsh¹,

Jeyaseelan²,

Li³

et al. 2020

Medical Image Analysis

724

358

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The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions to the problems of scarce and weak annotations. We hope this review increases the community awareness of the techniques to handle imperfect datasets.

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“…Nevertheless, compared to the detection and segmentation of cystic lesions, the approximate segmentation of this organ avoids demanding performance burdens. Furthermore, there exists various work focusing on segmentation of whole pancreas [11,2] and their results are good enough as an initial step for our method. Further improvement of the accuracy of our method can be achieved by including demographic information for classification [5], which will be integrated as future work.…”

Section: Discussionmentioning

confidence: 99%

Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks

Reichert

Lin

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of which may develop into PDAC. Previous computer-aided developments have achieved certain accuracy for classification on segmented cystic lesions in CT. However, pancreatic cysts have a large variation in size and shape, and the precise segmentation of them remains rather challenging, which restricts the computer-aided interpretation of CT images acquired for differential diagnosis. We propose a computer-aided framework for early differential diagnosis of pancreatic cysts without pre-segmenting the lesions using densely-connected convolutional networks (Dense-Net). The Dense-Net learns high-level features from whole abnormal pancreas and builds mappings between medical imaging appearance to different pathological types of pancreatic cysts. To enhance the clinical applicability, we integrate saliency maps in the framework to assist the physicians to understand the decision of the deep learning method. The test on a cohort of 206 patients with 4 pathologically confirmed subtypes of pancreatic cysts has achieved an overall accuracy of 72.8%, which is significantly higher than the baseline accuracy of 48.1%, which strongly supports the clinical potential of our developed method.

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

Cited by 41 publications

References 17 publications

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks

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