IODA: An input/output deep architecture for image labeling

International audienceIn this article, we propose a deep neural network (DNN) architecture called Input Output Deep Architecture (IODA) for solving the problem of image labeling. IODA directly links a whole image to a whole label map, assigning a label to each pixel using a single neural network forward step. Instead of designing a handcrafted a priori model on labels (such as an atlas in the medical domain), we propose to automatically learn the dependencies between labels. The originality of IODA is to transpose DNN input pre-training trick to the output space, in order to learn a high level representation of labels. It allows a fast image labeling inside a fully neural network framework, without the need of any preprocessing such as feature designing or output coding. In this article, IODA is applied on both a toy texture problem and a real-world medical image dataset, showing promising results. We provide an open source implementation of IODA 12

Mesure de l'hétérogénéité diffuse de la perfusion cérébrale en tomographie d'émission mono-photonique

L'hétérogénéité diffuse de la perfusion cérébrale en tomographie d émission monophotonique (TEMP) provient d'un phénomène physiologique dont l'origine est pour l'instant inconnue, et qui vient se superposer sur les images TEMP aux biais de cette technique. Nous avons développé notre propre algorithme basé sur la théorie des Marches Aléatoires (MA). Il a été validé sur des simulations numériques, pour la mesure de l'hétérogénéité diffuse de la perfusion cérébrale en TEMP dans un contexte de présence de defects focaux de perfusion. Le classement d'une population de 42 sujets réels par la MA est corrélé à la perception du consensus médical. L'algorithme de la MA a été comparé a des algorithmes standard provenant de l'analyse de texture (1er et 2ème ordre) ainsi que de l'analyse fractale. Nous avons finalement analysé les différences de perfusion et d'hétérogénéité sur des images de perfusion cérébrale d'une population de 19 patients atteints de leucémie et durant la chimiothérapie.Diffuse heterogeneity of brain perfusion in single photon emission tomography (SPECT) images comes from an unknown physiological phenomenon. Heterogeneity is superimposed with all biases of this modality on the images. We developed an algorithm based on the Random Walk (RW) theory. It has been validated on numerical simulations, for the quantification of diffuse heterogeneity of brain SPECT perfusion images in the presence of focal perfusion defect. The classification of 42 subjects by RW is correlated to the perception of the medical consensus. The RW algorithm was compared to standards algorithms based on texture and fractal analysis. Finally, we analyzed 19 brain SPECT perfusion exams of leukemic patients during chemotherapy to see evolution of the brain perfusion and the evolution of the diffuse brain heterogeneity.ROUEN-BU Sciences Madrillet (765752101) / SudocSudocFranceF

Multi-task Deep Learning Based CT Imaging Analysis For COVID-19 Pneumonia: Classification and Segmentation

International audienc

Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

International audienceThis paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification

Corrélation entre les zones les plus hypermétaboliques par FDG (HOTSPOT) et la fixation F-MISO dans les cancers bronchiques non à petites cellules

International audienc

A software tool for the comparison of GTV segmentation algorithms on PET/CT images

International audienc

Segmentation de tumeurs pulmonaires en TEP au 18F-FDG à l'aide de la marche aléatoire : application pour le suivi thérapeutique par radiothérapie externe.

National audienc

Radiomics-net: Convolutional Neural Networks on FDG PET Images for predicting cancer treatment response

International audience324Objectives: The aim was i) to develop and validate three convolutional neural network architectures (CNN) for predicting response to cancer treatment in FDG PET imaging, and ii) to compare their performances with three random forest classifiers. Methods: We have developed an end-to-end 3D convolutional neural network (3D-CNN). We have also evaluated 2 others CNN architectures from the literature (Ypsilantis et al. PloS, 2015). The first one, called 1S-CNN, corresponds to an architecture where the input of the CNN corresponds to one slice. The process is repeated on each slice belonging to the tumor. Then, the model is evaluated by a majority vote. The second one, called 3S-CNN takes a triplet of adjacent slices as input. The CNN architectures were evaluated on a retrospective study database of initial FDG PET images of 97 patients with an esophageal cancer treated by chemo-radiotherapy. 56 patients responded 3 months after treatment. FDG positive tissues were segmented using a fixed threshold value of 40% of SUVmax. Images were spatially normalized with isotropic voxels (2x2x2 mm3) followed by an absolute intensity resampling (0.5 SUV/bin). For each CNN, the search for the best architecture was achieved using a validation procedure, by tuning hyper parameters, such as the number of layers, the number of feature maps and the size of filters among others. For comparison, a radiomic analysis was also conducted from 17 uncorrelated features (Spearman’s rank correlation coefficient < 0.8, p<0.05) using 3 random forest classifiers: without feature selection (WFS), with a selection strategy based on a genetic algorithm (GARF) and based on the forest’s importance coefficient (FIC) (Desbordes et al. PloS One 2017). A five-fold cross-validation was performed (57 patients for training, 20 for validation, 20 for test). The performances of the methods were evaluated after each cross-validation process including i) the accuracy (Acc) of the model corresponding to the percentage of patients correctly classified (responder vs. non responder) and ii) a receiver operating characteristic curve analysis computing the area under the curve (AUC), sensitivity (Se) and specificity (Sp). P-value < 0.05 was considered statistically significant. Results: The performances of the CNN architectures outperformed those found with the RF classifiers. The best results were found with 3D-CNN and 3S-CNN with comparable performances: Acc=0.72±0.08, AUC=0.70±0.04, Se=0.79±0.17 and Sp=0.62±0.21 (3D-CNN). 1S-CNN, seems to have lower performances (Acc=0.67±0.06, AUC=0.67±0.06), but the 1S-CNN ROC curve was not statistically significantly different from 3D-CNN (p=0.53) and 3S-CNN (p=0.48) ROC curves. From the RF classifiers, the best results were found with the GARF algorithm (Acc=0.68±0.08, Se=0.79±0.10, Sp=0.41±0.11, AUC=0.59±0.06). GARF ROC curve was not statistically significantly different from 1S-CNN (p=0.10) and 3S-CNN (p=0.058) ROC curves, while the 3D-CNN ROC curve gave better statistically significant results compared to GARF ROC curve (p=0.028). Conclusion: CNN architectures give very promising results for predicting cancer treatment response on initial PET tumor images. These results need to be confirmed with different type of cancers