Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI

• URL: http://arxiv.org/abs/2205.01675v1
• Date: Mon, 2 May 2022 20:43:14 GMT
• Title: Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI
• Authors: Razieh Faghihpirayesh, Davood Karimi, Deniz Erdogmus, Ali Gholipour
• Abstract summary: We analyze the speed-accuracy performance of a variety of deep neural network models. We devised a symbolically small convolutional neural network that combines spatial details at high resolution with context features extracted at lower resolutions. We trained our model as well as eight alternative, state-of-the-art networks with manually-labeled fetal brain MRI slices.
• Score: 15.530500862944818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fetal brain segmentation is an important first step for slice-level motion correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering. To address this critical unmet need, in this work we analyzed the speed-accuracy performance of a variety of deep neural network models, and devised a symbolically small convolutional neural network that combines spatial details at high resolution with context features extracted at lower resolutions. We used multiple branches with skip connections to maintain high accuracy while devising a parallel combination of convolution and pooling operations as an input downsampling module to further reduce inference time. We trained our model as well as eight alternative, state-of-the-art networks with manually-labeled fetal brain MRI slices and tested on two sets of normal and challenging test cases. Experimental results show that our network achieved the highest accuracy and lowest inference time among all of the compared state-of-the-art real-time segmentation methods. We achieved average Dice scores of 97.99\% and 84.04\% on the normal and challenging test sets, respectively, with an inference time of 3.36 milliseconds per image on an NVIDIA GeForce RTX 2080 Ti. Code, data, and the trained models are available at https://github.com/bchimagine/real_time_fetal_brain_segmentation.

Related papers

• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume [2.3633885460047765]
  Current anomaly detection methods excel with benchmark industrial data but struggle with medical data due to varying definitions of 'normal' and 'abnormal' We propose a framework called Simple Slice-based Network (SimpleSliceNet), which utilizes a model pre-trained on ImageNet and fine-tuned on a separate MRI dataset as a 2D slice feature extractor to reduce computational cost.
  arXiv  Detail & Related papers  (2024-08-28T17:20:56Z)
• A self-attention model for robust rigid slice-to-volume registration of functional MRI [4.615338063719135]
  Head motion during fMRI scans can result in distortion, biased analyses, and increased costs. We introduce an end-to-end SVR model for aligning 2D fMRI slices with a 3D reference volume. Our model achieves competitive performance in terms of alignment accuracy compared to state-of-the-art deep learning-based methods.
  arXiv  Detail & Related papers  (2024-04-06T08:02:18Z)
• Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier [1.487444917213389]
  This paper proposed a lightweight 3D convolutional neural network (CNN) based framework for schizophrenia diagnosis using MRI images. The model achieves the highest accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall 94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-11-05T10:27:37Z)
• GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction [50.248694764703714]
  Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. We propose Greedy LEarning for Accelerated MRI reconstruction, an efficient training strategy for high-dimensional imaging settings.
  arXiv  Detail & Related papers  (2022-07-18T06:01:29Z)
• Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation [13.158995287578316]
  We propose a dynamic architecture network named Med-DANet to achieve effective accuracy and efficiency trade-off. For each slice of the input 3D MRI volume, our proposed method learns a slice-specific decision by the Decision Network. Our proposed method achieves comparable or better results than previous state-of-the-art methods for 3D MRI brain tumor segmentation.
  arXiv  Detail & Related papers  (2022-06-14T03:25:58Z)
• Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation [51.916106055115755]
  We propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN) Our architecture is composed of three sequential modules that are estimated together during training. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners.
  arXiv  Detail & Related papers  (2021-07-06T14:50:03Z)
• DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation [0.0]
  We propose a novel Dimension Fusion Edge-guided network (DFENet) that can meet both of these requirements by fusing the features of 2D and 3D CNNs. The proposed model is robust, accurate, superior to the existing methods, and can be relied upon for biomedical applications.
  arXiv  Detail & Related papers  (2021-05-17T15:43:59Z)
• Fast accuracy estimation of deep learning based multi-class musical source separation [79.10962538141445]
  We propose a method to evaluate the separability of instruments in any dataset without training and tuning a neural network. Based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches.
  arXiv  Detail & Related papers  (2020-10-19T13:05:08Z)
• Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset [81.02949933048332]
  This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of Representational Similarity Analysis (RSA) DRSL is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects.
  arXiv  Detail & Related papers  (2020-09-28T18:30:14Z)
• 4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation [49.32653090178743]
  We investigate whether extending this problem to full 4D deep learning using a history of MRI volumes can improve performance. We find that our proposed architecture outperforms previous approaches with a lesion-wise true positive rate of 0.84 at a lesion-wise false positive rate of 0.19.
  arXiv  Detail & Related papers  (2020-04-20T11:41:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.