An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

Lyksborg, Mark; Puonti, Oula; Agn, Mikael; Larsen, Rasmus

doi:10.1007/978-3-319-19665-7_17

Cited by 98 publications

(70 citation statements)

References 3 publications

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“…The proposed model ends with an ensembling stage, which combines prediction masks coming from the two networks of stage two. A higher segmentation accuracy is indeed demonstrated by combining more models with respect to the use of the single ones [27,28].…”

Section: Stagementioning

confidence: 92%

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Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

Santini¹,

Moreau²,

Rubeaux³

2019

Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19

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Precise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The need for accurate and automatic delineation tools is at the origin of the KiTS19 challenge. It aims at accelerating the research and development in this field to aid prognosis and treatment planning by providing a characterized dataset of 300 CT scans to be segmented. To address the challenge, we proposed an automatic, multi-stage, 2.5D deep learning-based segmentation approach based on Residual UNet framework. An ensambling operation is added at the end to combine prediction results from previous stages reducing the variance between single models. Our neural network segmentation algorithm reaches a mean Dice score of 0.96 and 0.74 for kidney and kidney tumors, respectively on 90 unseen test cases. The results obtained are promising and could be improved by incorporating prior knowledge about the benign cysts that regularly lower the tumor segmentation results.

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Section: Stagementioning

confidence: 92%

“…The use of diverse combinations of initializations, training settings and network architectures generally help subsequent ensembling operations [27,28]. For this reason, we carried out different training configurations (especially for stage 2) in order to reduce the generalization error of the final prediction.…”

Section: Training Settingsmentioning

confidence: 99%

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

Santini¹,

Moreau²,

Rubeaux³

2019

Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19

View full text Add to dashboard Cite

show abstract

“…Zhang et al [26] and Yoo et al [112] employed N3 algorithms on their clinical dataset. Similarly, Pereira et al [22] used them in both BRATS 2013 and 2015 Challenges, Lyksborg et al [121] in BRATS 2014, and Zikic et al [122] in BRATS 2013. Brain MRI datasets might have volumes acquired from different scanner vendors and also from the same scanner but with different protocols.…”

Section: Pre-processingmentioning

confidence: 99%

Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review

Bernal

Kushibar

Asfaw

et al. 2019

Artificial Intelligence in Medicine

342

172

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In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.

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“…However given the insufficiency of medical image data, it often becomes difficult to use deeper and more complex networks. Application of ConvNets in gliomas have been mostly reported for the segmentation of abnormal regions from 2D or 3D MRIs [30][31][32][33][34][35] . Automated detection and extraction of High Grade Gliomas (HGG) was performed using ConvNets 36 .…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection and Classification from Multi-sequence MRI: Study Using ConvNets

Banerjee

Masulli

Rovetta

2019

Lecture Notes in Computer Science

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Glioma constitutes 80% of malignant primary brain tumors in adults, and is usually classified as High Grade Glioma (HGG) and Low Grade Glioma (LGG). The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing brain tumors. Therefore, development of automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence. In this paper, we thoroughly investigate the power of Deep Convolutional Neural Networks (ConvNets) for classification of brain tumors using multi-sequence MR images. We propose novel ConvNet models, which are trained from scratch, on MRI patches, slices, and multi-planar volumetric slices. The suitability of transfer learning for the task is next studied by applying two existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset, through fine-tuning of the last few layers. Leave-one-patient-out (LOPO) testing, and testing on the holdout dataset are used to evaluate the performance of the ConvNets. Results demonstrate that the proposed ConvNets achieve better accuracy in all cases where the model is trained on the multi-planar volumetric dataset. Unlike conventional models, it obtains a testing accuracy of 95% for the low/high grade glioma classification problem. A score of 97% is generated for classification of LGG with/without 1p/19q codeletion, without any additional effort towards extraction and selection of features. We study the properties of self-learned kernels/ filters in different layers, through visualization of the intermediate layer outputs. We also compare the results with that of state-of-the-art methods, demonstrating a maximum improvement of 7% on the grading performance of ConvNets and 9% on the prediction of 1p/19q codeletion status.

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An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

Cited by 98 publications

References 3 publications

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review

Brain Tumor Detection and Classification from Multi-sequence MRI: Study Using ConvNets

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