Automatic segmentation of the clinical target volume and organs at risk in the planning <scp>CT</scp> for rectal cancer using deep dilated convolutional neural networks

Men, Kuo; Dai, Jianrong; Li, Yexiong

doi:10.1002/mp.12602

Cited by 263 publications

(246 citation statements)

References 75 publications

(101 reference statements)

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“…[28][29][30][31][32] Bladder segmentation was also addressed with deep learning techniques, however, the image modality of study has mainly been limited to computed tomography (CT). [33][34][35] For example, Cha et al 33 proposed a CNN followed by a level-set method to segment the IW and OW. Considering the significant advantages of MRI, including its high soft-tissue contrast and nonradiation, it may be more suitable for the characterization of bladder wall and tumor properties.…”

Section: Introductionmentioning

confidence: 99%

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Dolz

Rony

et al. 2018

Medical Physics

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Purpose Precise segmentation of bladder walls and tumor regions is an essential step toward noninvasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC). However, the automatic delineation of bladder walls and tumor in magnetic resonance images (MRI) is a challenging task, due to important bladder shape variations, strong intensity inhomogeneity in urine, and very high variability across the population, particularly on tumors’ appearance. To tackle these issues, we propose to leverage the representation capacity of deep fully convolutional neural networks. Methods The proposed network includes dilated convolutions to increase the receptive field without incurring extra cost or degrading its performance. Furthermore, we introduce progressive dilations in each convolutional block, thereby enabling extensive receptive fields without the need for large dilation rates. The proposed network is evaluated on 3.0T T2‐weighted MRI scans from 60 pathologically confirmed patients with BC. Results Experiments show the proposed model to achieve a higher level of accuracy than state‐of‐the‐art methods, with a mean Dice similarity coefficient of 0.98, 0.84, and 0.69 for inner wall, outer wall, and tumor region segmentation, respectively. These results represent a strong agreement with reference contours and an increase in performance compared to existing methods. In addition, inference times are less than a second for a whole three‐dimensional (3D) volume, which is between two and three orders of magnitude faster than related state‐of‐the‐art methods for this application. Conclusion We showed that a CNN can yield precise segmentation of bladder walls and tumors in BC patients on MRI. The whole segmentation process is fully automatic and yields results similar to the reference standard, demonstrating the viability of deep learning models for the automatic multiregion segmentation of bladder cancer MRI images.

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Section: Introductionmentioning

confidence: 99%

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Dolz

Rony

et al. 2018

Medical Physics

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“…Deep convolutional neural networks (CNNs) have recently been shown to be well‐suited for image classification problems based on unstructured highly dimensional data . Recently, axial two‐dimensional (2D) CNN strategies have shown promise for autosegmentation in radiotherapy . However, 2D convolutions disregard information about neighboring slices, which can lead to prediction errors in less well‐defined organs.…”

Section: Introductionmentioning

confidence: 99%

“…3D CNNs have the ability to train on a richer and more holistic learning environment, but are much more memory‐intensive than 2D models . Previous studies have incorporated dilated convolutional layers into their architectures in series and in parallel, which serve to increase the perceptive field of the network while simultaneously preserving memory . Dilated convolutions are particularly useful when contouring mobile structures such as brain tumors, since they benefit from having a broad perceptive field in all hierarchal levels .…”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16][17][18] Recently, axial two-dimensional (2D) CNN strategies have shown promise for autosegmentation in radiotherapy. [19][20][21][22][23][24] However, 2D convolutions disregard information about neighboring slices, which can lead to prediction errors in less well-defined organs. In response, some groups have implemented orthogonal 2D CNNs into their prediction strategies.…”

Section: Introductionmentioning

confidence: 99%

“…15,30,31 Previous studies have incorporated dilated convolutional layers into their architectures in series and in parallel, which serve to increase the perceptive field of the network while simultaneously preserving memory. 20,32,33 Dilated convolutions are particularly useful when contouring mobile structures such as brain tumors, since they benefit from having a broad perceptive field in all hierarchal levels. 32,34 Dilated convolutions are a low-memory solution to having a high perceptive field at all stages of the network, but since dilated convolutions are sparse they can skip over information, which can render them disadvantageous for less well-defined structures.…”

Section: Introductionmentioning

confidence: 99%

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A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning

et al. 2019

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Purpose This study suggests a lifelong learning‐based convolutional neural network (LL‐CNN) algorithm as a superior alternative to single‐task learning approaches for automatic segmentation of head and neck (OARs) organs at risk. Methods and materials Lifelong learning‐based convolutional neural network was trained on twelve head and neck OARs simultaneously using a multitask learning framework. Once the weights of the shared network were established, the final multitask convolutional layer was replaced by a single‐task convolutional layer. The single‐task transfer learning network was trained on each OAR separately with early stoppage. The accuracy of LL‐CNN was assessed based on Dice score and root‐mean‐square error (RMSE) compared to manually delineated contours set as the gold standard. LL‐CNN was compared with 2D‐UNet, 3D‐UNet, a single‐task CNN (ST‐CNN), and a pure multitask CNN (MT‐CNN). Training, validation, and testing followed Kaggle competition rules, where 160 patients were used for training, 20 were used for internal validation, and 20 in a separate test set were used to report final prediction accuracies. Results On average contours generated with LL‐CNN had higher Dice coefficients and lower RMSE than 2D‐UNet, 3D‐Unet, ST‐ CNN, and MT‐CNN. LL‐CNN required ~72 hrs to train using a distributed learning framework on 2 Nvidia 1080Ti graphics processing units. LL‐CNN required 20 s to predict all 12 OARs, which was approximately as fast as the fastest alternative methods with the exception of MT‐CNN. Conclusions This study demonstrated that for head and neck organs at risk, LL‐CNN achieves a prediction accuracy superior to all alternative algorithms.

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Deep Learning and Precision Medicine

Selvasudha¹,

Khan²,

Ganesan³

et al. 2022

Deep Learning for Targeted Treatments

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Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks

Abstract: These data suggest that DDCNN can be used to segment the CTV and OARs accurately and efficiently. It was invariant to the body size, body shape, and age of the patients. DDCNN could improve the consistency of contouring and streamline radiotherapy workflows.

Cited by 263 publications

References 75 publications

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning

Deep Learning and Precision Medicine

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