Attention-guided deep learning for gestational age prediction using fetal brain MRI

Shen, Liyue; Zheng, Jimmy; Lee, Edward H.; Shpanskaya, Katie; McKenna, Emily; Atluri, Mahesh G; Plasto, Dinko; Mitchell, Courtney; Lai, Liangzhen; Guimaraes, Carolina V.; Dahmoush, Hisham; Chueh, Jane; Halabi, Safwan; Pauly, John M.; Li, Xing; Lu, Kun; Öztekin, Özgür; Kline‐Fath, Beth M.; Yeom, Kristen W.

doi:10.1038/s41598-022-05468-5

Cited by 24 publications

(23 citation statements)

References 45 publications

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“…To validate the superior predictive performance of the proposed PSA-guided dynamic feature fusion CNN (PDFF-CNN) for gestational age prediction, we compared it with existing convolutional neural networks such as ResNet, 45 SE-Net, 46 RepVGG, 47 and EPSANet, 35 along with the methods proposed by Shen et al 21 and Liao et al 19 We chose the methods of the mentioned scholars because Shen's method uses attention mechanisms to enhance the focus on important features, while Liao's method applies deformable convolution to the VGG16 network, both of which are effective in extracting multi-orientation gestational age features. To further demonstrate the effectiveness of PDFF-CNN, ablation experiments were conducted, and the feature extraction process was visualized.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…However, most studies have been limited to relatively balanced brain MRI datasets or isolated anatomical regions. 51,52 Recent studies 18,21 reflecting the effectiveness of using fetal brain MRI single-slice strategies as well as multislice or 3D strategies in gestational age prediction have shown that single-slice strategies have higher prediction accuracy than multislice or 3D strategies. Inspired by this, we decided to use only single coronal fetal brain MRI slices in our experiments.…”

Section: Discussionmentioning

confidence: 99%

“…They proposed a basic regressor model based on ResNet-18, which uses a private dataset with cropped 2D images covering fetal regions in the input images to train ResNet with overall global features, effectively helping to extract local image features to predict fetal gestational age. Shen et al 21 developed an attention-guided multiplane ResNet-50 model to predict gestational age. By combining crucial features of multiview MRI and applying age-activated weakly supervised attention mapping to achieve angular positioning of fetal brain characteristics under background noise, the attention-guided ResNet model can identify the region of the fetal brain.…”

Section: Introductionmentioning

confidence: 99%

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PDFF‐CNN: An attention‐guided dynamic multi‐orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset

Feng,

Zhou,

Xia

et al. 2023

Medical Physics

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BackgroundFetal brain magnetic resonance imaging (MRI)‐based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brain malformations.PurposeThe uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above‐mentioned problems pose great challenges for deep learning‐based fetal brain MRI gestational age prediction.MethodsIn this study, a pyramid squeeze attention (PSA)‐guided dynamic feature fusion CNN (PDFF‐CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF‐CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi‐orientation feature representations in a parallel weight‐sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi‐orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age prediction performance.ResultsEvaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2‐weighted MRI images from 157 subjects, PDFF‐CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an R2 of 0.904. Furthermore, the attention activation maps of PDFF‐CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestational stage.ConclusionsThese results suggest that the proposed PDFF‐CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PDFF‐CNN: An attention‐guided dynamic multi‐orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset

Feng,

Zhou,

Xia

et al. 2023

Medical Physics

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“…However, SFOA is very sensitive to initializing and premature convergence [ 33 ]. Moreover, in MGA, the predictions are made based on single-slice inputs, hypothetically restraining the information available to the network [ 34 ].…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Machine Learning Approach for Brain MRI Classification

2022

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Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space’s dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.

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“…Shen et al [ 21 ] used attention-guided, multi-plane ResNet-50 models trained on Stanford data to predict the gestational age. They trained various CNN models based on only imaging features for the prediction of gestational age.…”

Section: Introductionmentioning

confidence: 99%

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

Mazher

Qayyum

Puig

et al. 2022

Entropy

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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.

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Attention-guided deep learning for gestational age prediction using fetal brain MRI

Cited by 24 publications

References 45 publications

PDFF‐CNN: An attention‐guided dynamic multi‐orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset

PDFF‐CNN: An attention‐guided dynamic multi‐orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset

An Enhanced Machine Learning Approach for Brain MRI Classification

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

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