A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation

Roth, Holger R.; Shen, Chen; Oda, Hirotaka; Sugino, Takaaki; Oda, Masahiro; Hayashi, Yuichiro; Misawa, Kazunari; Mori, Kensaku

doi:10.1007/978-3-030-00937-3_48

Cited by 118 publications

(139 citation statements)

References 19 publications

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“…Under this consideration, cutting‐edge technology based on 3D U‐Net using a two‐stage, coarse‐to‐fine approach (two 3D U‐Nets with a cascade structure, which we call Cascade U‐Nets) to accomplish a multiple organ segmentation was proposed and showed state‐of‐the‐art performance. We compared our method with this novel approach based on the same training (228) and testing (12) CT scans. The experiment showed the accuracies (IUs) in each target type on average of 12 testing CT scans obtained using our method were better than a single 3D U‐Net with ROI‐sized inputs, and still better than the Cascade U‐Nets for nine target types, except for the other nine types of target (having a small volume or tube structures).…”

Section: Discussionmentioning

confidence: 99%

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Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

et al. 2017

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Purpose: We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. Methods: We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment. Results: The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth. Conclusions: We propose a single network based on pixel-to-label deep learning to address the challenging issue of anatomical structure segmentation in 3D CT cases. The novelty of this work is the policy of deep learning of the different 2D sectional appearances of 3D anatomical structures for CT cases and the majority voting of the 3D segmentation results from multiple crossed 2D sections to achieve availability and reliability with better efficiency, generality, and flexibility than conventional segmentation methods, which must be guided by human expertise.

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Section: Discussionmentioning

confidence: 99%

“…The drawback of our network is the poor accuracy (IUs) when segmenting smaller structures. This will be improved in future by using a larger training dataset and new network structures . We will also expand the proposed network to other imaging modalities such as FDG‐PET and MR images.…”

Section: Discussionmentioning

confidence: 99%

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

et al. 2017

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“…pancreas). Furthermore, deep learning approaches are much faster than conventional methods [4], [39], [40].…”

Section: B Multi-organ Segmentationmentioning

confidence: 99%

“…The method proposed by Tong et al [37] is much faster than the one proposed by Wolz et al [36]. The 3D FCN proposed by Roth et al [4] is the state-of-the-art method based on deep CNNs. It is clear that the 3D FCN achieves significantly better results in the pancreas segmentation.…”

Section: B Multi-organ Segmentationmentioning

confidence: 99%

“…Within medical imaging, the problem of image segmentation has been one of the major challenges. Segmentation is a pre-requisite for many different types of clinical applications, including brain segmentation [2], cardiac ventricle segmentation [3], abdominal organ segmentation [4], and cell segmentation in biological images [5]. In these applications, the results of the segmentation are usually used to derive quantitative measurements or biomarkers for subsequent diagnosis and treatment planning.…”

Section: Introductionmentioning

confidence: 99%

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DRINet for Medical Image Segmentation

Chen

Bentley

Mori

et al. 2018

IEEE Trans. Med. Imaging

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Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Ut purus elit, vestibulum ut, placerat ac, adipiscing vitae, felis. Curabitur dictum gravida mauris. Nam arcu libero, nonummy eget, consectetuer id, vulputate a, magna. Donec vehicula augue eu neque. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Mauris ut leo. Cras viverra metus rhoncus sem. Nulla et lectus vestibulum urna fringilla ultrices. Phasellus eu tellus sit amet tortor gravida placerat. Integer sapien est, iaculis in, pretium quis, viverra ac, nunc. Praesent eget sem vel leo ultrices bibendum. Aenean faucibus. Morbi dolor nulla, malesuada eu, pulvinar at, mollis ac, nulla. Curabitur auctor semper nulla. Donec varius orci eget risus. Duis nibh mi, congue eu, accumsan eleifend, sagittis quis, diam. Duis eget orci sit amet orci dignissim rutrum.Nam dui ligula, fringilla a, euismod sodales, sollicitudin vel, wisi. Morbi auctor lorem non justo. Nam lacus libero, pretium at, lobortis vitae, ultricies et, tellus. Donec aliquet, tortor sed accumsan bibendum, erat ligula aliquet magna, vitae ornare odio metus a mi. Abstract-Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The UNet architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual Inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid (CSF) on brain CT images, multi-organ segmentation on abdominal CT images, multi-class brain tumour segmentation on MR images.Index Terms-Convolutional neural network, medical image segmentation, brain atrophy, abdominal organ segmentation.

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SABOS‐Net: Self‐supervised attention based network for automatic organ segmentation of head and neck CT images

Francis

Pooloth

Singam

et al. 2022

Int J Imaging Syst Tech

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The segmentation of Organs At Risk (OAR) in Computed Tomography (CT) images is an essential part of the planning phase of radiation treatment to avoid the adverse effects of cancer radiotherapy treatment. Accurate segmentation is a tedious task in the head and neck region due to a large number of small and sensitive organs and the low contrast of CT images. Deep learningbased automatic contouring algorithms can ease this task even when the organs have irregular shapes and size variations. This paper proposes a fully automatic deep learning-based self-supervised 3D Residual UNet architecture with CBAM(Convolution Block Attention Mechanism) for the organ segmentation in head and neck CT images. The Model Genesis structure and image context restoration techniques are used for self-supervision, which can help the network learn image features from unlabeled data, hence solving the annotated medical data scarcity problem in deep networks. A new loss function is applied for training by integrating Focal loss, Tversky loss, and Cross-entropy loss. The proposed model outperforms the state-of-the-art methods in terms of dice similarity coefficient in segmenting the organs. Our self-supervised model could achieve a 4% increase in the dice score of Chiasm, which is a small organ that is present only in a very few CT slices. The proposed model exhibited better accuracy for 5 out of 7 OARs than the recent state-of-the-art models. The proposed model could simultaneously segment all seven organs in an average time of 0.

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A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation

Cited by 118 publications

References 19 publications

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

DRINet for Medical Image Segmentation

SABOS‐Net: Self‐supervised attention based network for automatic organ segmentation of head and neck CT images

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