Lesion segmentation in lung CT scans using unsupervised adversarial learning

Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments

Towards realistic laparoscopic image generation using image-domain translation

none5openMarzullo, Aldo; Moccia, Sara; Catellani, Michele; Calimeri, Francesco; Momi, Elena DeMarzullo, Aldo; Moccia, Sara; Catellani, Michele; Calimeri, Francesco; Momi, Elena D

Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC). During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on Convolutional Neural Networks (CNNs). Methods: The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks($m_1$) and Mask-RCNN($m_2$), which are fed with single still-frames $I(t)$. The other two models ($M_1$, $M_2$) are modifications of the former ones consisting on the addition of a stage which makes use of 3D Convolutions to process temporal information. $M_1$, $M_2$ are fed with triplets of frames ($I(t-1)$, $I(t)$, $I(t+1)$) to produce the segmentation for $I(t)$. Results: The proposed method was evaluated using a custom dataset of 11 videos (2,673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion: The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in presence of poor visibility, occasional bleeding, or specular reflections

Spike-mediated viral membrane fusion is inhibited by a specific anti-IFITM2 monoclonal antibody

The early steps of viral infection involve protein complexes and structural lipid rearrangements which characterize the peculiar strategies of each virus to invade permissive host cells. Members of the human immune-related interferon-induced transmembrane (IFITM) protein family have been described as inhibitors of the entry of a broad range of viruses into the host cells. Recently, it has been shown that SARS-CoV-2 is able to hijack IFITM2 for efficient infection. Here, we report the characterization of a newly generated specific anti-IFITM2 mAb able to impair Spike-mediated internalization of SARS-CoV-2 in host cells and, consequently, to reduce the SARS-CoV-2 cytopathic effects and syncytia formation. Furthermore, the anti-IFITM2 mAb reduced HSVs- and RSV-dependent cytopathic effects, suggesting that the IFITM2-mediated mechanism of host cell invasion might be shared with other viruses besides SARS-CoV-2. These results show the specific role of IFITM2 in mediating viral entry into the host cell and its candidacy as a cell target for antiviral therapeutic strategies

Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma

Purpose Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation.Methods In this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied.Results We obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)-area under curve = 0.81(0.03), accuracy = 0.87(0.07), precision = 0.88(0.07), recall = 0.88(0.07) and F1-score = 0.87(0.07), while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome.Conclusions All these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model

Apprentissage profond et théorie des graphes pour l'analyse de la connectivité cérébrale dans la sclérose en plaques

Multiple sclerosis (MS) is a chronic disease of the central nervous system, leading cause of nontraumatic disability in young adults. MS is characterized by inflammation, demyelination and neurodegenrative pathological processes which cause a wide range of symptoms, including cognitive deficits and irreversible disability. Concerning the diagnosis of the disease, the introduction of Magnetic Resonance Imaging (MRI) has constituted an important revolution in the last 30 years. Furthermore, advanced MRI techniques, such as brain volumetry, magnetization transfer imaging (MTI) and diffusion-tensor imaging (DTI) are nowadays the main tools for detecting alterations outside visible brain lesions and contributed to our understanding of the pathological mechanisms occurring in normal appearing white matter. In particular, new approaches based on the representation of MR images of the brain as graph have been used to study and quantify damages in the brain white matter network, achieving promising results. In the last decade, novel deep learning based approaches have been used for studying social networks, and recently opened new perspectives in neuroscience for the study of functional and structural brain connectivity. Due to their effectiveness in analyzing large amount of data, detecting latent patterns and establishing functional relationships between input and output, these artificial intelligence techniques have gained particular attention in the scientific community and is nowadays widely applied in many context, including computer vision, speech recognition, medical diagnosis, among others. In this work, deep learning methods were developed to support biomedical image analysis, in particular for the classification and the characterization of MS patients based on structural connectivity information. Graph theory, indeed, constitutes a sensitive tool to analyze the brain networks and can be combined with novel deep learning techniques to detect latent structural properties useful to investigate the progression of the disease. In the first part of this manuscript, an overview of the state of the art will be given. We will focus our analysis on studies showing the interest of DTI for WM characterization in MS. An overview of the main deep learning techniques will be also provided, along with examples of application in the biomedical domain. In a second part, two deep learning approaches will be proposed, for the generation of new, unseen, MRI slices of the human brain and for the automatic detection of the optic disc in retinal fundus images. In the third part, graph-based deep learning techniques will be applied to the study of brain structural connectivity of MS patients. Graph Neural Network methods to classify MS patients in their respective clinical profiles were proposed with particular attention to the model interpretation, the identification of potentially relevant brain substructures, and to the investigation of the importance of local graph-derived metrics for the classification task. Semisupervised and unsupervised approaches were also investigated with the aim of reducing the human intervention in the pipelineLa sclérose en plaques (SEP) est une maladie chronique du système nerveux central, principale cause de handicap d'origine non traumatique chez l'adulte jeune. Il se caractérise par de nombreux processus de démyélinisation inflammatoire qui provoquent une vaste gamme de symptômes, notamment des déficits cognitifs et invalidité irréversible. L'imagerie par résonance magnétique (IRM) est aujourd'hui l'outil de référence pour le diagnostic de la maladie. L'emploi de techniques d'imagerie avancées comme la spectroscopie par résonance magnétique et l'IRM de diffusion (DTI) sont les principaux outils de détection des altérations autres que les lésions cérébrales visibles. Ces techniques ont également permis de mieux comprendre mécanismes pathologiques dans la substance blanche. En particulier, de nouvelles approches basées sur la représentation d'images IRM utilisant la théorie des graphes ont été appliquées avec succès pour l'étude et la quantification des dommages à la substance blanche. La dernière décennie a vu l'émergence de prometteuses méthodes d'apprentissage profond pour l'étude des réseaux sociaux. Ces méthodes ont ouvert des perspectives fascinantes en neurosciences pour l'étude de la connectivité structurelle et fonctionnelle du cerveau. Grâce à leur capacité à analyser d'énormes quantités de données et à identifier les relations latentes, ce domaine de l'intelligence artificielle a connu un assez grand succès dans la communauté scientifique et s'applique désormais dans de nombreux contextes, notamment le diagnostic médical. Dans ce manuscrit, nous présenterons les différentes techniques d'apprentissage profond développées dans ce travail concernant l'analyse des images biomédicales et, en particulier, pour la classification et la caractérisation des patients atteints de SEP. Dans ce contexte, la connectivité structurelle est utilisée pour représenter les patients. En fait, la théorie des graphes est devenue un outil sensible pour la détection des altérations causées par les pathologies cérébrales, et peut être combinée à des techniques d'apprentissage automatique afin d'identifier les propriétés structurelles latentes utiles pour étudier la progression de la maladie. La première partie de ce manuscrit est consacré à la description de l'état de l'art. Cet état de l'art se focalisera sur les études montrant les effets de la SEP sur les faisceaux de SB grâce à l'emploi de l'imagerie de tenseur de diffusion. Une description des principales techniques d'apprentissage profond sera également fournie, ainsi que des exemples d'applicabilité dans le contexte biomédical. Dans la seconde partie, deux techniques d'apprentissage profond seront proposées, concernant la génération de nouvelles images IRM du cerveau humain et l'identification automatique du disque optique dans les images du fond oculaire. Dans la troisième partie, nous présenterons les techniques d'apprentissage profond combinées à la théorie des graphiques que développée dans ce travail pour étudier la connectivité structurelle des patients atteints d'une SEP. Nous présenterons en particulier des modèles de réseaux de neurones basés sur la théorie des graphes pour la classification des patients dans leurs formes cliniques. Une attention particulière sera accordée à l'interprétation de ces modèles afin d'identifier les sous-structures cérébrales potentiellement importantes. Enfin, nous explorerons des approches semi-supervisées et non supervisées pour réduire l'intervention humaine dans les processus de décisio