8 research outputs found
Influence of segmentation on deep iris recognition performance
Despite the rise of deep learning in numerous areas of computer vision and
image processing, iris recognition has not benefited considerably from these
trends so far. Most of the existing research on deep iris recognition is
focused on new models for generating discriminative and robust iris
representations and relies on methodologies akin to traditional iris
recognition pipelines. Hence, the proposed models do not approach iris
recognition in an end-to-end manner, but rather use standard heuristic iris
segmentation (and unwrapping) techniques to produce normalized inputs for the
deep learning models. However, because deep learning is able to model very
complex data distributions and nonlinear data changes, an obvious question
arises. How important is the use of traditional segmentation methods in a deep
learning setting? To answer this question, we present in this paper an
empirical analysis of the impact of iris segmentation on the performance of
deep learning models using a simple two stage pipeline consisting of a
segmentation and a recognition step. We evaluate how the accuracy of
segmentation influences recognition performance but also examine if
segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for
the experiments and report several interesting findings.Comment: 6 pages, 3 figures, 3 tables, submitted to IWBF 201
Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
The iris can be considered as one of the most important biometric traits due
to its high degree of uniqueness. Iris-based biometrics applications depend
mainly on the iris segmentation whose suitability is not robust for different
environments such as near-infrared (NIR) and visible (VIS) ones. In this paper,
two approaches for robust iris segmentation based on Fully Convolutional
Networks (FCNs) and Generative Adversarial Networks (GANs) are described.
Similar to a common convolutional network, but without the fully connected
layers (i.e., the classification layers), an FCN employs at its end a
combination of pooling layers from different convolutional layers. Based on the
game theory, a GAN is designed as two networks competing with each other to
generate the best segmentation. The proposed segmentation networks achieved
promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4,
IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in
both non-cooperative and cooperative domains, outperforming the baselines
techniques which are the best ones found so far in the literature, i.e., a new
state of the art for these datasets. Furthermore, we manually labeled 2,431
images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks
available for research purposes.Comment: Accepted for presentation at the Conference on Graphics, Patterns and
Images (SIBGRAPI) 201
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
Automatic Segmentation of Intramedullary Multiple Sclerosis Lesions
Contexte: La moelle Ă©piniĂšre est un composant essentiel du systĂšme nerveux central. Elle contient des neurones responsables dâimportantes fonctionnalitĂ©s et assure la transmission dâinformations motrices et sensorielles entre le cerveau et le systĂšme nerveux pĂ©riphĂ©rique. Un endommagement de la moelle Ă©piniĂšre, causĂ© par un choc ou une maladie neurodĂ©gĂ©nĂ©rative, peut mener Ă un sĂ©rieux handicap, pouvant entraĂźner des incapacitĂ©s fonctionnelles, de la paralysie et/ou de la douleur. Chez les patients atteints de sclĂ©rose en plaques (SEP), la moelle Ă©piniĂšre est frĂ©quemment affectĂ©e par de lâatrophie et/ou des lĂ©sions. Lâimagerie par rĂ©sonance magnĂ©tique (IRM) conventionnelle est largement utilisĂ©e par des chercheurs et des cliniciens pour Ă©valuer et caractĂ©riser, de façon non-invasive, des altĂ©rations micro-structurelles. Une Ă©valuation quantitative des atteintes structurelles portĂ©es Ă la moelle Ă©piniĂšre (e.g. sĂ©vĂ©ritĂ© de lâatrophie, extension des lĂ©sions) est essentielle pour le diagnostic, le pronostic et la supervision sur le long terme de maladies, telles que la SEP. De plus, le dĂ©veloppement de biomarqueurs impartiaux est indispensable pour Ă©valuer lâeffet de nouveaux traitements thĂ©rapeutiques. La segmentation de la moelle Ă©piniĂšre et des lĂ©sions intramĂ©dullaires de SEP sont, par consĂ©quent, pertinentes dâun point de vue clinique, aussi bien quâune Ă©tape nĂ©cessaire vers lâinterprĂ©tation dâimages RM multiparamĂ©triques. Cependant, la segmentation manuelle est une tĂąche extrĂȘmement chronophage, fastidieuse et sujette Ă des variations inter- et intra-expert. Il y a par consĂ©quent un besoin dâautomatiser les mĂ©thodes de segmentations, ce qui pourrait faciliter lâefficacitĂ© procĂ©dures dâanalyses. La segmentation automatique de lĂ©sions est compliquĂ© pour plusieurs raisons: (i) la variabilitĂ© des lĂ©sions en termes de forme, taille et position, (ii) les contours des lĂ©sions sont la plupart du temps difficilement discernables, (iii) lâintensitĂ© des lĂ©sions sur des images MR sont similaires Ă celles de structures visiblement saines. En plus de cela, rĂ©aliser une segmentation rigoureuse sur lâensemble dâune base de donnĂ©es multi-centrique dâIRM est rendue difficile par lâimportante variabilitĂ© des protocoles dâacquisition (e.g. rĂ©solution, orientation, champ de vue de lâimage). MalgrĂ© de considĂ©rables rĂ©cents dĂ©veloppements dans le traitement dâimages MR de moelle Ă©piniĂšre, il nây a toujours pas de mĂ©thode disponible pouvant fournir une segmentation rigoureuse et fiable de la moelle Ă©piniĂšre pour un large spectre de pathologies et de protocoles dâacquisition. Concernant les lĂ©sions intramĂ©dullaires, une recherche approfondie dans la littĂ©rature nâa pas pu fournir une mĂ©thode disponible de segmentation automatique.
Objectif: Développer un systÚme complÚtement automatique pour segmenter la moelle épiniÚre et les lésions intramédullaires sur des IRM conventionnelles humaines.
MĂ©thode: Lâapproche prĂ©sentĂ©e est basĂ©e de deux rĂ©seaux de neurones Ă convolution mis en cascade. La mĂ©thode a Ă©tĂ© pensĂ©e pour faire face aux principaux obstacles que prĂ©sentent les donnĂ©es IRM de moelle Ă©piniĂšre. Le procĂ©dĂ© de segmentation a Ă©tĂ© entrainĂ© et validĂ© sur une base de donnĂ©es privĂ©e composĂ©e de 1943 images, acquises dans 30 diffĂ©rents centres avec des protocoles hĂ©tĂ©rogĂšnes. Les sujets scannĂ©s comportent 459 sujets sains, 471 patients SEP et 112 avec dâautres pathologies affectant la moelle Ă©piniĂšre. Le module de segmentation de la moelle Ă©piniĂšre a Ă©tĂ© comparĂ© Ă une mĂ©thode existante reconnue par la communautĂ©, PropSeg.
RĂ©sultats: Lâapproche basĂ©e sur les rĂ©seaux de neurones Ă convolution a fourni de meilleurs rĂ©sultats que PropSeg, atteignant un Dice mĂ©dian (intervalle inter-quartiles) de 94.6 (4.6) vs. 87.9 (18.3) %. Pour les lĂ©sions, notre segmentation automatique a permis d'obtenir un Dice de 60.0 (21.4) % en le comparant Ă la segmentation manuelle, un ratio de vrai positifs de 83 (34) %, et une prĂ©cision de 77 (44) %.
Conclusion: Une mĂ©thode complĂštement automatique et innovante pour segmenter la moelle Ă©piniĂšre et les lĂ©sions SEP intramĂ©dullaires sur des donnĂ©es IRM a Ă©tĂ© conçue durant ce projet de maĂźtrise. La mĂ©thode a Ă©tĂ© abondamment validĂ©e sur une base de donnĂ©es clinique. La robustesse de la mĂ©thode de segmentation de moelle Ă©piniĂšre a Ă©tĂ© dĂ©montrĂ©e, mĂȘme sur des cas pathologiques. Concernant la segmentation des lĂ©sions, les rĂ©sultats sont encourageants, malgrĂ© un taux de faux positifs relativement Ă©levĂ©. Je crois en lâimpact que peut potentiellement avoir ces outils pour la communautĂ© de chercheurs. Dans cette optique, les mĂ©thodes ont Ă©tĂ© intĂ©grĂ©es et documentĂ©es dans un logiciel en accĂšs-ouvert, la âSpinal Cord Toolboxâ. Certains des outils dĂ©veloppĂ©s pendant ce projet de MaĂźtrise sont dĂ©jĂ utilisĂ©s par des analyses dâĂ©tudes cliniques, portant sur des patients SEP et sclĂ©rose latĂ©rale amyotrophique.----------ABSTRACT
Context: The spinal cord is a key component of the central nervous system, which contains neurons responsible for complex functions, and ensures the conduction of motor and sensory information between the brain and the peripheral nervous system. Damage to the spinal cord, through trauma or neurodegenerative diseases, can lead to severe impairment, including functional disabilities, paralysis and/or pain. In multiple sclerosis (MS) patients, the spinal cord is frequently affected by atrophy and/or lesions. Conventional magnetic resonance imaging (MRI) is widely used by researchers and clinicians to non-invasively assess and characterize spinal cord microstructural changes. Quantitative assessment of the structural damage to the spinal cord (e.g. atrophy severity, lesion extent) is essential for the diagnosis, prognosis and longitudinal monitoring of diseases, such as MS. Furthermore, the development of objective biomarkers is essential to evaluate the effect of new therapeutic treatments. Spinal cord and intramedullary MS lesions segmentation is consequently clinically relevant, as well as a necessary step towards the interpretation of multi-parametric MR images. However, manual segmentation is highly time-consuming, tedious and prone to intra- and inter-rater variability. There is therefore a need for automated segmentation methods to facilitate the efficiency of analysis pipelines. Automatic lesion segmentation is challenging for various reasons: (i) lesion variability in terms of shape, size and location, (ii) lesion boundaries are most of the time not well defined, (iii) lesion intensities on MR data are confounding with those of normal-appearing structures. Moreover, achieving robust segmentation across multi-center MRI data is challenging because of the broad variability of data features (e.g. resolution, orientation, field of view). Despite recent substantial developments in spinal cord MRI processing, there is still no method available that can yield robust and reliable spinal cord segmentation across the very diverse spinal pathologies and data features. Regarding the intramedullary lesions, a thorough search of the relevant literature did not yield available method of automatic segmentation.
Goal: To develop a fully-automatic framework for segmenting the spinal cord and intramedullary MS lesions from conventional human MRI data.
Method: The presented approach is based on a cascade of two Convolutional Neural Networks (CNN). The method has been designed to face the main challenges of âreal worldâ spinal cord MRI data. It was trained and validated on a private dataset made up of 1943 MR volumes, acquired in different 30 sites with heterogeneous acquisition protocols. Scanned subjects involve 459 healthy controls, 471 MS patients and 112 with other spinal pathologies. The proposed spinal cord segmentation method was compared to a state-of-the-art spinal cord segmentation method, PropSeg.
Results: The CNN-based approach achieved better results than PropSeg, yielding a median (interquartile range) Dice of 94.6 (4.6) vs. 87.9 (18.3) % when compared to the manual segmentation. For the lesion segmentation task, our method provided a median Dice-overlap with the manual segmentation of 60.0 (21.4) %, a lesion-based true positive rate of 83 (34) % and a lesion-based precision de 77 (44) %.
Conclusion: An original fully-automatic method to segment the spinal cord and intramedullary MS lesions on MRI data has been devised during this Masterâs project. The method was validated extensively against a clinical dataset. The robustness of the spinal cord segmentation has been demonstrated, even on challenging pathological cases. Regarding the lesion segmentation, the results are encouraging despite the fairly high false positive rate. I believe in the potential value of these developed tools for the research community. In this vein, the methods are integrated and documented into an open-source software, the Spinal Cord Toolbox. Some of the tools developed during this Masterâs project are already integrated into automated analysis pipelines of clinical studies, including MS and Amyotrophic Lateral Sclerosis patients