Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Trebeschi, Stefano; Griethuysen, Joost J.M. van; Lambregts, Doenja M. J.; Lahaye, Max J.; Parmar, Chintan; Bakers, Frans C. H.; Peters, Niek A.; Beets‐Tan, Regina G. H.; Aerts, Hugo J.W.L.

doi:10.1038/s41598-017-05728-9

Cited by 238 publications

(160 citation statements)

References 32 publications

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“…[104][105][106][107] DL involves abstraction by building networks with >2 processing layers. 93,[110][111][112] Figure 3 shows examples of CNNs, which can be used to classify data types as diverse as purchasing preferences and satellite images. The first CNN was proposed by LeCun et al in 1998, 109 but its success was limited until the advent of graphic processing units and the development of learning algorithms.…”

Section: Deep Learningmentioning

confidence: 99%

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Napel

Jardim‐Perassi

et al. 2018

Cancer

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148

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Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

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Section: Deep Learningmentioning

confidence: 99%

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Napel

Jardim‐Perassi

et al. 2018

Cancer

Self Cite

148

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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%

“…[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%

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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“…They exploited CNN features learned from nonmedical domain. Trebeschi et al constructed automatic segmentation system for rectal cancer based on the CNN model . However, to the best of our knowledge, no study has yet been performed on the T category classification of rectal tumors using deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network

Kim

Lee

et al. 2019

Int J Imaging Syst Tech

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Preoperative chemoradiotherapy is known to reduce the local recurrence of locally advanced rectal cancer. However, the careful use of preoperative chemoradiotherapy is essential, because unnecessary over‐treatment can result in unintended complications. Therefore, a diagnostic system for distinguishing between T2 and T3 rectal cancers should be developed. According to the diagnostic criteria for rectal cancer, radiologists first identify the locations and the shapes of both the tumor and the rectum from a medical image and then diagnose the T2/T3 rectal cancer by determining whether the tumor passes through the rectal wall or not. We construct two distinct convolutional neural network models to achieve the automated segmentation of each rectum and tumor, respectively. Then, we construct another convolutional neural network model, which uses the output images of segmentation models as input and determines whether the tumor in the input magnetic resonance image is at the T2 stage or at the T3 stage. We evaluate the effectiveness of the proposed method based on 290 magnetic resonance images from 133 subjects. The proposed model demonstrates an accuracy of 94%.

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Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Cited by 238 publications

References 32 publications

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

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

Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network

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