Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

Zhou, Xiangrong; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

doi:10.1117/12.2254201

Cited by 17 publications

(14 citation statements)

References 12 publications

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“…Following [1], the 2D images from all views were used during the training process. Using TensorFlow 0.12, we trained the FCNN with those matrices.…”

Section: Methodsmentioning

confidence: 99%

“…The output hard segmentations were derived from channel number with maximum intensities on the heatmaps after the softmax layers. Specifically, our FCNN is structurally identical to the one used in [1]. …”

Section: Figurementioning

confidence: 99%

“…Traditionally, multi-atlas methods have been able to segment abdominal organs with reasonable accuracy [1, 5, 6, 10, 11]. More recently, researchers have demonstrated that fully convolutional neural networks (FCNN) show great promise in both general image segmentation and abdominal organ segmentation of CT scans [1, 12, 13]. Recently, Zhou et al [1] showed that fully convolutional networks produce excellent results in abdominal organ segmentation of CT scans.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, researchers have demonstrated that fully convolutional neural networks (FCNN) show great promise in both general image segmentation and abdominal organ segmentation of CT scans [1, 12, 13]. Recently, Zhou et al [1] showed that fully convolutional networks produce excellent results in abdominal organ segmentation of CT scans. Yet, deep learning approaches have not been applied to whole abdomen MRI segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, inspired by Zhou et al [1], we propose to use a fully convolutional neural network (FCNN) to perform abdominal organ segmentation of T2w MR images. Two different types of segmentation tasks were employed to evaluate the performance of FCNN on T2w MR images.…”

Section: Introductionmentioning

confidence: 99%

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Fully convolutional neural networks improve abdominal organ segmentation

Bobo

Bao

Huo

et al. 2018

Medical Imaging 2018: Image Processing

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Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI’s with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI’s acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI’s with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities.

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“…Following [1], the 2D images from all views were used during the training process. Using TensorFlow 0.12, we trained the FCNN with those matrices.…”

Section: Methodsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fully convolutional neural networks improve abdominal organ segmentation

Bobo

Bao

Huo

et al. 2018

Medical Imaging 2018: Image Processing

View full text Add to dashboard Cite

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Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches

Zhou

2020

Advances in Experimental Medicine and Biology

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Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks

Xie

Zaidi

2019

Eur Radiol

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Objectives The conceptus dose during diagnostic imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. The aim of this work is to develop a methodology for automated construction of patient-specific computational phantoms based on actual patient CT images to enable accurate estimation of conceptus dose. Methods We developed a 3D deep convolutional network algorithm for automated segmentation of CT images to build realistic computational phantoms. The neural network architecture consists of analysis and synthesis paths with four resolution levels each, trained on manually labeled CT scans of six identified anatomical structures. Thirty-two CT exams were augmented to 128 datasets and randomly split into 80%/20% for training/testing. The absorbed doses for six segmented organs/tissues from abdominal CT scans were estimated using Monte Carlo calculations. The resulting radiation doses were then compared between the computational models generated using automated segmentation and manual segmentation, serving as reference. Results The Dice similarity coefficient for identified internal organs between manual segmentation and automated segmentation results varies from 0.92 to 0.98 while the mean Hausdorff distance for the uterus is 16.1 mm. The mean absorbed dose for the uterus is 2.9 mGy whereas the mean organ dose differences between manual and automated segmentation techniques are 0.07%, − 0.45%, − 1.55%, − 0.48%, − 0.12%, and 0.28% for the kidney, liver, lung, skeleton, uterus, and total body, respectively. Conclusion The proposed methodology allows automated construction of realistic computational models that can be exploited to estimate patient-specific organ radiation doses from radiological imaging procedures. Key Points • The conceptus dose during diagnostic radiology and nuclear medicine imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. • The proposed methodology allows automated construction of realistic computational models that can be exploited to estimate patient-specific organ radiation doses from radiological imaging procedures. • The dosimetric results can be used for the risk-benefit analysis of radiation hazards to conceptus from diagnostic imaging procedures, thus guiding the decision-making process.

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Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

Cited by 17 publications

References 12 publications

Fully convolutional neural networks improve abdominal organ segmentation

Fully convolutional neural networks improve abdominal organ segmentation

Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches

Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks

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