Abstract
Magnetic resonance imaging (MRI) is a prevailing method of modal infant brain tissue analysis that precisely segments brain tissue and is vitally important for diagnosis, remediation, and analysis of early brain development. To achieve such segmentation is challenging, particularly for the brain of a six-month-old, owing to several factors: poor image quality; isointense contrast between white and gray matter and the simple incomplete volume consequence of a tiny brain size; and discrepancies in brain tissues, illumination settings, and the vagarious region. This article addresses these challenges with a fuzzy-informed deep learning segmentation network that takes T1- and T2-weighted MRIs as inputs. First, a fuzzy logic layer encodes input to the fuzzy domain. Second, a volumetric fuzzy pooling (VFP) layer models the local fuzziness of the volumetric convolutional maps by applying fuzzification, accumulation, and defuzzification on the adjacency feature map neighborhoods. Third, the VFP layer is employed to design the fuzzy-enabled multiscale feature learning module to enable the extraction of brain features in different receptive fields. Finally, we redesign the Project & Excite module using the VPF layer to enable modeling uncertainty during feature recalibration, and a comprehensive training paradigm is used to learn the ideal parameters of every building block. Extensive experimental comparative studies substantiate the efficiency and accuracy of the proposed model in terms of different evaluation metrics to solve multimodal infant brain segmentation problems on the iSeg-2017 dataset.