1 research outputs found
RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation
Optical coherence tomography (OCT) helps ophthalmologists assess macular
edema, accumulation of fluids, and lesions at microscopic resolution.
Quantification of retinal fluids is necessary for OCT-guided treatment
management, which relies on a precise image segmentation step. As manual
analysis of retinal fluids is a time-consuming, subjective, and error-prone
task, there is increasing demand for fast and robust automatic solutions. In
this study, a new convolutional neural architecture named RetiFluidNet is
proposed for multi-class retinal fluid segmentation. The model benefits from
hierarchical representation learning of textural, contextual, and edge features
using a new self-adaptive dual-attention (SDA) module, multiple self-adaptive
attention-based skip connections (SASC), and a novel multi-scale deep self
supervision learning (DSL) scheme. The attention mechanism in the proposed SDA
module enables the model to automatically extract deformation-aware
representations at different levels, and the introduced SASC paths further
consider spatial-channel interdependencies for concatenation of counterpart
encoder and decoder units, which improve representational capability.
RetiFluidNet is also optimized using a joint loss function comprising a
weighted version of dice overlap and edge-preserved connectivity-based losses,
where several hierarchical stages of multi-scale local losses are integrated
into the optimization process. The model is validated based on three publicly
available datasets: RETOUCH, OPTIMA, and DUKE, with comparisons against several
baselines. Experimental results on the datasets prove the effectiveness of the
proposed model in retinal OCT fluid segmentation and reveal that the suggested
method is more effective than existing state-of-the-art fluid segmentation
algorithms in adapting to retinal OCT scans recorded by various image scanning
instruments.Comment: 11 pages, Early Access Version, IEEE Transactions on Medical Imagin