Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

Abstract

To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)—GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model’s encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791±0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients’ survival days.

Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

“…fractal dimension texture analysis (FDTA), gray-level run length matrix (GLRLM), Fourier power spectrum (FPS), gray-level size zone matrix (GLSZM), higher order spectra (HOS), and local binary pattern (LPB). 30 These radiomics features approach is shown in Figure 6.…”

“…The Geometrical features are Length and coordinates, First axis, Second axis, Third axis, Centroid coordinates, Eigenvalues, Equatorial eccentricity, and Meridional eccentricity. The further textural radiomics feature extracted from 3D MRI modalities input volume are (1) first‐order statistics/statistical features (FOS/SF), gray‐level co‐occurrence matrix (GLCM/SGLDM), gray‐level difference statistics (GLDS), neighborhood gray‐tone difference matrix (NGTDM), statistical feature matrix (SFM), Laws texture energy measures (LTE/TEM), fractal dimension texture analysis (FDTA), gray‐level run length matrix (GLRLM), Fourier power spectrum (FPS), gray‐level size zone matrix (GLSZM), higher order spectra (HOS), and local binary pattern (LPB) 30 . These radiomics features approach is shown in Figure 6.…”