Automatic Brain Organ Segmentation with 3D Fully Convolutional Neural Network for Radiation Therapy Treatment Planning

Duanmu, Hongyi; Kim, Jinkoo; Kanakaraj, Praitayini; Wang, Andrew; Joshua, John; Kong, Jun; Wang, Fusheng

doi:10.1109/isbi45749.2020.9098485

Cited by 8 publications

(11 citation statements)

References 13 publications

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“…Convolutional neural networks (CNN) are a class of artificial neural networks, i.e., data-driven models inspired by biological systems which are shown to greatly benefit fields relying on computer vision, such as medical imaging ( Lundervold and Lundervold, 2019 ). They are being successfully applied in tasks requiring recognition (segmentation) of brain structures, including the ones involving the optic chiasm ( Ibragimov and Xing, 2017 ; Tong et al, 2018 ; Chen et al, 2019 ; Zhu et al, 2019 ; Duanmu et al, 2020 ; Mlynarski et al, 2020 ). This is in particular true for the attempts using MRI data, which have been demonstrated to provide superior contrast and recognition of optic chiasm boundaries compared to other imaging techniques, such as computer tomography ( Ibragimov and Xing, 2017 ; Duanmu et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…They are being successfully applied in tasks requiring recognition (segmentation) of brain structures, including the ones involving the optic chiasm ( Ibragimov and Xing, 2017 ; Tong et al, 2018 ; Chen et al, 2019 ; Zhu et al, 2019 ; Duanmu et al, 2020 ; Mlynarski et al, 2020 ). This is in particular true for the attempts using MRI data, which have been demonstrated to provide superior contrast and recognition of optic chiasm boundaries compared to other imaging techniques, such as computer tomography ( Ibragimov and Xing, 2017 ; Duanmu et al, 2020 ). The CNNs, however, are not a universal tool, as their performance is largely dependent on both the quantity and quality of the training data.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images

2021

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Convolutional neural network (CNN) models are of great promise to aid the segmentation and analysis of brain structures. Here, we tested whether CNN trained to segment normal optic chiasms from the T1w magnetic resonance imaging (MRI) image can be also applied to abnormal chiasms, specifically with optic nerve misrouting as typical for human albinism. We performed supervised training of the CNN on the T1w images of control participants (n = 1049) from the Human Connectome Project (HCP) repository and automatically generated algorithm-based optic chiasm masks. The trained CNN was subsequently tested on data of persons with albinism (PWA; n = 9) and controls (n = 8) from the CHIASM repository. The quality of outcome segmentation was assessed via the comparison to manually defined optic chiasm masks using the Dice similarity coefficient (DSC). The results revealed contrasting quality of masks obtained for control (mean DSC ± SEM = 0.75 ± 0.03) and PWA data (0.43 ± 0.8, few-corrected p = 0.04). The fact that the CNN recognition of the optic chiasm fails for chiasm abnormalities in PWA underlines the fundamental differences in their spatial features. This finding provides proof of concept for a novel deep-learning-based diagnostics approach of chiasmal misrouting from T1w images, as well as further analyses on chiasmal misrouting and their impact on the structure and function of the visual system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images

2021

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“…We also compare the performance of our proposed network and the other state-of-the-art CNN architectures. The semicircular canal can be segmented effectively with the proposed method, so can the other organs in the future, for example, facial nerve [ 27 ], cochleae [ 28 ], and spinal cord [ 29 ]. …”

Section: Introductionmentioning

confidence: 99%

“…The semicircular canal can be segmented effectively with the proposed method, so can the other organs in the future, for example, facial nerve [ 27 ], cochleae [ 28 ], and spinal cord [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U‐Net with Attention Mechanism

Liu

Chen

et al. 2021

Computational Intelligence and Neuroscience

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The vestibular system is the sensory apparatus that helps the body maintain its postural equilibrium, and semicircular canal is an important organ of the vestibular system. The semicircular canals are three membranous tubes, each forming approximately two-thirds of a circle with a diameter of approximately 6.5 mm, and segmenting them accurately is of great benefit for auxiliary diagnosis, surgery, and treatment of vestibular disease. However, the semicircular canal has small volume, which accounts for less than 1% of the overall computed tomography image. Doctors have to annotate the image in a slice-by-slice manner, which is time-consuming and labor-intensive. To solve this problem, we propose a novel 3D convolutional neural network based on 3D U-Net to automatically segment the semicircular canal. We added the spatial attention mechanism of 3D spatial squeeze and excitation modules, as well as channel attention mechanism of 3D global attention upsample modules to improve the network performance. Our network achieved an average dice coefficient of 92.5% on the test dataset, which shows competitive performance in semicircular canals segmentation task.

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“…However, manual delineation of brain anatomical structures and healthy organs is time-consuming and error-prone. Thus, developing a fully automatic segmentation algorithm will effectively improve the efficiency and consistency of radiotherapy planning [3], which is exactly what the Anatomical Brain Barriers to Cancer Spread (ABCs) Challenge is aimed at.…”

Section: Introductionmentioning

confidence: 99%

Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread

Zou

Dou

2021

Segmentation, Classification, and Registration of Multi-Modality Medical Imaging Data

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It is important to accurately segment anatomical brain barriers to cancer spread with multi-modal images, in order to assist definition of the clinical target volume (CTV). In this work, we explore a multi-modal segmentation method largely driven by domain knowledge. We apply 3D U-Net as the backbone model. In order to reduce the learning difficulty of deep convolutional neural networks, we employ a label merging strategy for the symmetrical structures which have both left and right labels, to highlight the structural information regardless of the locations. Moreover, considering the existence of visual preference for certain modality and mismatches in co-registration, we adopt a multi-modality ensemble strategy for multi-modal learning to enable the models better driven by domain knowledge of this task, which is different from fully data-driven methods, like early fusion strategy for multi-modal images. By contrast, multi-modality ensemble strategy yields better segmentation results. Our method achieved an average score of 0.895 on MICCAI 2020 Anatomical Brain Barriers to Cancer Spread Challenge's final test dataset (https://abcs.mgh.harvard.edu/.). Detailed methodologies and results are described in this technical report (This work was done when X. Zou did remote internship with CUHK.).

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Automatic Brain Organ Segmentation with 3D Fully Convolutional Neural Network for Radiation Therapy Treatment Planning

Cited by 8 publications

References 13 publications

Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images

Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images

Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U‐Net with Attention Mechanism

Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread

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