# A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

arXiv: https://arxiv.org/abs/2004.12847 · HTML: https://mlsanity.com/paper/2004.12847/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Convolution-Free Medical Image Segmentation using Transformers](https://mlsanity.com/paper/2102.13645/) (2021)
- [Learning to segment fetal brain tissue from noisy annotations](https://mlsanity.com/paper/2203.14962/) (2022)
- [CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI](https://mlsanity.com/paper/2205.08239/) (2022)
- [Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation](https://mlsanity.com/paper/2111.04737/) (2021)
- [Detailed delineation of the fetal brain in diffusion MRI via multi-task learning](https://mlsanity.com/paper/2409.06716/) (2024)
- [FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom](https://mlsanity.com/paper/2109.03624/) (2021)
- [Anatomically Constrained Tractography of the Fetal Brain](https://mlsanity.com/paper/2403.02444/) (2024)
- [AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation](https://mlsanity.com/paper/2202.13687/) (2022)
- [Train-Free Segmentation in MRI with Cubical Persistent Homology](https://mlsanity.com/paper/2401.01160/) (2024)
- [Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)](https://mlsanity.com/paper/2601.15759/) (2026)
- [Multipath CNN with alpha matte inference for knee tissue segmentation from MRI](https://mlsanity.com/paper/2109.14249/) (2021)
- [AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI](https://mlsanity.com/paper/2411.02867/) (2024)
- [Architectural Design and Performance Analysis of FPGA based AI Accelerators: A Comprehensive Review](https://mlsanity.com/paper/2603.08740/) (2026)

## Abstract (arXiv)

Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. We evaluated our method quantitatively based on several performance measures and expert evaluations. Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique. We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned in the gestational age range of 16 to 39 weeks. With a computation time of less than 1 minute per fetal brain, our method can facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.