3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context

We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. In this paper we introduce a CNN-based model which efficiently combines the advantages of the short-range 3D context and the long-range 2D context. To overcome the limitations of specific choices of neural network architectures, we also propose to merge outputs of several cascaded 2D-3D models by a voxelwise voting strategy. Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences. Finally, a simple and efficient algorithm for training large CNN models is introduced. We evaluate our method on the public benchmark of the BRATS 2017 challenge on the task of multiclass segmentation of malignant brain tumors. Our method achieves good performances and produces accurate segmentations with median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854 (enhancing core). Our approach can be naturally applied to various tasks involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic

Deep Learning with Mixed Supervision for Brain Tumor Segmentation

International audienceMost of the current state-of-the-art methods for tumor segmentation are based on machine learning models trained on manually segmented images. This type of training data is particularly costly, as manual delineation of tumors is not only time-consuming but also requires medical expertise. On the other hand, images with a provided global label (indicating presence or absence of a tumor) are less informative but can be obtained at a substantially lower cost. In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation. The idea of our approach is to extend segmentation networks with an additional branch performing image-level classification. The model is jointly trained for segmentation and classification tasks in order to exploit information contained in weakly-annotated images while preventing the network to learn features which are irrelevant for the segmentation task. We evaluate our method on the challenging task of brain tumor seg-mentation in Magnetic Resonance images from BRATS 2018 challenge. We show that the proposed approach provides a significant improvement of seg-mentation performance compared to the standard supervised learning. The observed improvement is proportional to the ratio between weakly-annotated and fully-annotated images available for training

Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy

International audiencePlanning of radiotherapy involves accurate segmentation of a large number of organs at risk (OAR), i.e., organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of OAR inside the head, from magnetic resonance images (MRIs). Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem, and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and nonexclusive classes, addressing problems related to computational costs and missing ground truth segmentations for a subset of classes. We enforce anatomical consistency of the result in a postprocessing step. In particular, we introduce a graph-based algorithm for segmentation of the optic nerves, enforcing the connectivity between the eyes and the optic chiasm. We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered OAR, which were originally used for radiotherapy planning. In addition, the segmentations produced by our model on an independent test set of 50 MRIs were evaluated by an experienced radiotherapist in order to qualitatively assess their accuracy. The mean distances between produced segmentations and the ground truth ranged from 0.1 to 0.7 mm across different organs. A vast majority (96%) of the produced segmentations were found acceptable for radiotherapy planning

Genetic polymorphisms in DNA base excision repair gene XRCC1 and the risk of squamous cell carcinoma of the head and neck

<p>Abstract</p> <p>Background</p> <p>The genes of base excision repair (BER) pathway have been extensively studied in the association with various human cancers. We performed a case-control study to test the association between two common single nucleotide polymorphisms (SNPs) of <it>XRCC1 </it>gene with human head and neck squamous cell carcinoma (HNSCC).</p> <p>Methods</p> <p>The genotype analysis of Arg194Trp and Arg399Gln gene polymorphisms for 92 HNSCC patients and 124 controls of cancer free subjects, in Polish population were performed using the PCR-based restriction fragment length polymorphism (PCR-RFLP) with endonuclease <it>Msp</it>I.</p> <p>Results</p> <p>No altered risk has been found individually for these SNPs, however haplotypes analysis showed high association with head and neck cancer. The highest frequency, according to wild-type of Arg194Arg and Arg399Arg genotypes, was identified for Arg194Trp-Arg399Arg haplotype (OR, 2.96; 95% CI, 1.01–8.80).</p> <p>Conclusion</p> <p>Finally, we identified the combined Arg194Trp-Arg399Arg genotype of base excision repair gene <it>XRCC1 </it>that was associated with HNSCC and may have an impact on identification of a high-risk cancer population.</p

Apprentissage profond pour la segmentation des tumeurs cérébrales et des organes à risque en radiothérapie

Medical images play an important role in cancer diagnosis and treatment. Oncologists analyze images to determine the different characteristics of the cancer, to plan the therapy and to observe the evolution of the disease. The objective of this thesis is to propose efficient methods for automatic segmentation of brain tumors and organs at risk in the context of radiotherapy planning, using Magnetic Resonance (MR) images. First, we focus on segmentation of brain tumors using Convolutional Neural Networks (CNN) trained on MRIs manually segmented by experts. We propose a segmentation model having a large 3D receptive field while being efficient in terms of computational complexity, based on combination of 2D and 3D CNNs. We also address problems related to the joint use of several MRI sequences (T1, T2, FLAIR). Second, we introduce a segmentation model which is trained using weakly-annotated images in addition to fully-annotated images (with voxelwise labels), which are usually available in very limited quantities due to their cost. We show that this mixed level of supervision considerably improves the segmentation accuracy when the number of fully-annotated images is limited.\\ Finally, we propose a methodology for an anatomy-consistent segmentation of organs at risk in the context of radiotherapy of brain tumors. The segmentations produced by our system on a set of MRIs acquired in the Centre Antoine Lacassagne (Nice, France) are evaluated by an experienced radiotherapist.Les images médicales jouent un rôle important dans le diagnostic et la prise en charge des cancers. Les oncologues analysent des images pour déterminer les différentes caractéristiques de la tumeur, pour proposer un traitement adapté et suivre l'évolution de la maladie. L'objectif de cette thèse est de proposer des méthodes efficaces de segmentation automatique des tumeurs cérébrales et des organes à risque dans le contexte de la radiothérapie, à partir des images de résonance magnétique (IRM). Premièrement, nous nous intéressons à la segmentation des tumeurs cérébrales en utilisant des réseaux neuronaux convolutifs entrainés sur des IRM segmentés par des experts. Nous proposons un modèle de segmentation ayant un grand champ récepteur 3D tout en étant efficace en termes de complexité de calcul, en combinant des réseaux neuronaux convolutifs 2D et 3D. Nous abordons aussi les problèmes liés à l'utilisation conjointe des différentes séquences IRM (T1, T2, FLAIR). Nous introduisons ensuite un modèle de segmentation qui est entrainé avec des images faiblement annotées en complément des images segmentées, souvent disponibles en quantités très limitées du fait de leur coût. Nous montrons que ce niveau mixte de supervision améliore considérablement la performance de segmentation quand le nombre d'images entièrement annotées est limité. Finalement, nous proposons une méthodologie pour segmenter, de manière cohérente anatomiquement, les organes à risque dans le contexte de la radiothérapie des tumeurs cérébrales. Les segmentations produites par notre système sur un ensemble d'IRM acquis dans le Centre Antoine Lacassagne (Nice) sont évaluées par un radiothérapeute expérimenté

Deep learning for segmentation of brain tumors and organs at risk in radiotherapy planning

Les images médicales jouent un rôle important dans le diagnostic et la prise en charge des cancers. Les oncologues analysent des images pour déterminer les différentes caractéristiques de la tumeur, pour proposer un traitement adapté et suivre l'évolution de la maladie. L'objectif de cette thèse est de proposer des méthodes efficaces de segmentation automatique des tumeurs cérébrales et des organes à risque dans le contexte de la radiothérapie, à partir des images de résonance magnétique (IRM). Premièrement, nous nous intéressons à la segmentation des tumeurs cérébrales en utilisant des réseaux neuronaux convolutifs entrainés sur des IRM segmentés par des experts. Nous proposons un modèle de segmentation ayant un grand champ récepteur 3D tout en étant efficace en termes de complexité de calcul, en combinant des réseaux neuronaux convolutifs 2D et 3D. Nous abordons aussi les problèmes liés à l'utilisation conjointe des différentes séquences IRM (T1, T2, FLAIR). Nous introduisons ensuite un modèle de segmentation qui est entrainé avec des images faiblement annotées en complément des images segmentées, souvent disponibles en quantités très limitées du fait de leur coût. Nous montrons que ce niveau mixte de supervision améliore considérablement la performance de segmentation quand le nombre d'images entièrement annotées est limité. Finalement, nous proposons une méthodologie pour segmenter, de manière cohérente anatomiquement, les organes à risque dans le contexte de la radiothérapie des tumeurs cérébrales. Les segmentations produites par notre système sur un ensemble d'IRM acquis dans le Centre Antoine Lacassagne (Nice) sont évaluées par un radiothérapeute expérimenté.Medical images play an important role in cancer diagnosis and treatment. Oncologists analyze images to determine the different characteristics of the cancer, to plan the therapy and to observe the evolution of the disease. The objective of this thesis is to propose efficient methods for automatic segmentation of brain tumors and organs at risk in the context of radiotherapy planning, using Magnetic Resonance (MR) images. First, we focus on segmentation of brain tumors using Convolutional Neural Networks (CNN) trained on MRIs manually segmented by experts. We propose a segmentation model having a large 3D receptive field while being efficient in terms of computational complexity, based on combination of 2D and 3D CNNs. We also address problems related to the joint use of several MRI sequences (T1, T2, FLAIR). Second, we introduce a segmentation model which is trained using weakly-annotated images in addition to fully-annotated images (with voxelwise labels), which are usually available in very limited quantities due to their cost. We show that this mixed level of supervision considerably improves the segmentation accuracy when the number of fully-annotated images is limited.\\ Finally, we propose a methodology for an anatomy-consistent segmentation of organs at risk in the context of radiotherapy of brain tumors. The segmentations produced by our system on a set of MRIs acquired in the Centre Antoine Lacassagne (Nice, France) are evaluated by an experienced radiotherapist

3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context

International audienceWe present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. In this paper we introduce a CNN-based model which efficiently combines the advantages of the short-range 3D context and the long-range 2D context. Furthermore, we propose a network architecture with modality-specific subnetworks in order to be more robust to the problem of missing MR sequences during the training phase. To overcome the limitations of specific choices of neural network architectures, we describe a hierarchical decision process to combine outputs of several segmentation models. Finally, a simple and efficient algorithm for training large CNN models is introduced. We evaluate our method on the public benchmark of the BRATS 2017 challenge on the task of multiclass segmentation of malignant brain tumors. Our method achieves good performances and produces accurate segmentations with median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854 (enhancing core)

Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks

In the revision process:Planning of radiotherapy involves accurate segmentation of a large number of organs at risk, i.e. organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of organs at risk inside the brain region, from Magnetic Resonance (MR) images. Our system performs segmenta-tion of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and non-exclusive classes, addressing problems related to computational costs and missing ground truth segmentations for a subset of classes. We enforce anatomical consistency of the result in a postprocessing step, in particular we introduce a graph-based algorithm for segmentation of the optic nerves, enforcing the connectivity between the eyes and the optic chiasm. We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered organs at risk, which were originally used for radiotherapy planning. In addition, the segmentations produced by our model on an independent test set of 50 MRIs are evaluated by an experienced radiotherapist in order to qualitatively assess their accuracy. The mean distances between produced segmentations and the ground truth ranged from 0.1 mm to 0.7 mm across different organs. A vast majority (96 %) of the produced segmentations were found acceptable for radiotherapy planning