End-to-end multi-task learning approaches for the joint epiretinal membrane segmentation and screening in OCT images. (June 2022)

• Record Type: Journal Article
• Authors: Gende, Mateo; Moura, Joaquim de; Novo, Jorge; Ortega, Marcos
• Is Part Of: Computerized medical imaging and graphics. Volume 98(2022)
• Publication Date: 2022-06
• Subjects: Computer-aided diagnosis -- Optical coherence tomography -- Epiretinal membrane -- Deep learning -- Multi-task learning; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102068
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Background and objectives: The Epiretinal Membrane (ERM) is an ocular disease that can cause visual distortions and irreversible vision loss. Patient sight preservation relies on an early diagnosis and on determining the location of the ERM in order to be treated and potentially removed. In this context, the visual inspection of the images in order to screen for ERM signs is a costly and subjective process. Methods: In this work, we propose and study three end-to-end fully-automatic approaches for the simultaneous segmentation and screening of ERM signs in Optical Coherence Tomography images. These convolutional approaches exploit a multi-task learning context to leverage inter-task complementarity in order to guide the training process. The proposed architectures are combined with three different state of the art encoder architectures of reference in order to provide an exhaustive study of the suitability of each of the approaches for these tasks. Furthermore, these architectures work in an end-to-end manner, entailing a significant simplification of the development process since they are able to be trained directly from annotated images without the need for a series of purpose-specific steps. Results: In terms of segmentation, the proposed models obtained a precision of 0.760 ± 0.050, a sensitivity of 0.768 ± 0.210 and a specificity of 0.945 ± 0.011. For the screening task, these models achieved a precision of 0.963 ± 0.068, a sensitivity of 0.816 ± 0.162 and a specificity of 0.983 ± 0.068. The obtained results show that these multi-task approaches are able to perform competitively with or even outperform single-task methods tailored for either the segmentation or the screening of the ERM. Conclusions: These results highlight the advantages of using complementary knowledge related to the segmentation and screening tasks in the diagnosis of this relevant pathology, constituting the first proposal to address the diagnosis of the ERM from a multi-task perspective. Graphical Abstract: ga1 Highlights: Three multi-task approaches for ERM screening and segmentation are proposed. These approaches were implemented into three end-to-end convolutional architectures. The results highlight the benefits of multi-task learning over single-task methods. Proposed approaches entail significant simplification compared to traditional methods. Multi-task ERM diagnosis can help an early detection of this relevant pathology.