Multi-Branch Deformable Convolutional Neural Network with Label Distribution Learning for Fetal Brain Age Prediction

Liao, Lufan; Zhang, Xin; Zhao, Fenqiang; Lou, Junjie; Wang, Li; Xu, Xiangmin; Zhang, He; Li, Gang

doi:10.1109/isbi45749.2020.9098553

Cited by 23 publications

(18 citation statements)

References 9 publications

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“…We also observe consistently better results for all models when using the brain rather than the trunk ROI, with best figures 0.683 (0.950) provided again by DenseNet-201. These results compare favourably with those reported by Liao et al (2020), 0.751 (0.947), and Shi et al (2020), 0.767 (0.920), and, in terms of MAE, they seem to do so by a substantial margin. Further to this, with the exception of R 2 for poorly performing MobileNet-v2 and GoogleLeNet, additional improvements are consistently observed for all models when combining volumetric brain and trunk information, a unique feature of the proposed technique, with MAE (R 2 ) 0.618 (0.958) for DenseNet-201.…”

Section: Clinical Application: Ga Predictionsupporting

confidence: 80%

“…We have shown that deep feature extraction using pre-trained models combined with correlation constrained linear regression provides accurate results for this task. Our results look superior to existing methods (Liao et al, 2020;Shi et al, 2020), particularly when complementing brain features with trunk information, but there are differences in the cohorts considered. Most notably, existing methods use larger cohorts including single-sequence data from 289 subjects (Liao et al, 2020) and multi-sequence data from 764 subjects (Shi et al, 2020).…”

Section: Discussionmentioning

confidence: 53%

“…Our results look superior to existing methods (Liao et al, 2020;Shi et al, 2020), particularly when complementing brain features with trunk information, but there are differences in the cohorts considered. Most notably, existing methods use larger cohorts including single-sequence data from 289 subjects (Liao et al, 2020) and multi-sequence data from 764 subjects (Shi et al, 2020). Small sample sizes in our case precluded isolation of a subset of subjects for testing, a potentially important limitation when compared to Shi et al (2020).…”

Section: Discussionmentioning

confidence: 53%

“…We compare the performance of RGDSVR and DSVR for GA estimation using a trunk ROI corresponding to the FOV returned by the default DSVR implementation. As existing methods for fetal GA estimation from MRI (Liao et al, 2020;Shi et al, 2020) are based on brain data, we also test the GA estimation performance using a brain ROI from our reconstructions. In all cases we use the GA estimation method described in § 2.8 with 3D space spanned by slices from N r = 200 random volume reorientations.…”

Section: Clinical Application: Ga Predictionmentioning

confidence: 99%

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Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction

Cordero‐Grande¹,

Ortuño-Fisac²,

Uus³

et al. 2021

Preprint

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Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructions integrated with a diffeomorphic volume to slice registration method. Experiments are performed to validate our contributions and compare with a state of the art method in a cohort of 72 fetal datasets in the range of 20-36 weeks gestational age. Results suggest improved image resolution and more accurate prediction of gestational age at scan when comparing to a state of the art reconstruction method. In addition, gestational age prediction results from our volumetric reconstructions compare favourably with existing brain-based approaches, with boosted accuracy when integrating information of organs other than the brain. Namely, a mean absolute error of 0.618 weeks (R 2 = 0.958) is achieved when combining fetal brain and trunk information.

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Section: Clinical Application: Ga Predictionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 53%

Section: Discussionmentioning

confidence: 53%

Section: Clinical Application: Ga Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction

Cordero‐Grande¹,

Ortuño-Fisac²,

Uus³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To ensure comparisons are performed using the same FOV, we assess the performance of RGDSVR and DSVR for GA estimation using a trunk ROI corresponding to the FOV used as the target reconstruction volume in the experiments in [11]. As existing methods for fetal GA estimation from MRI [14], [39], [40] are based on brain data, we also test the GA estimation performance using a brain ROI from our reconstructions. In all cases we use the GA estimation method described in § II-H with 3D space spanned by slices from N r = 200 random volume reorientations.…”

Section: E Clinically-oriented Application: Ga Predictionmentioning

confidence: 99%

Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration

Cordero‐Grande

Ortuno-Fisac

Hoyo³

et al. 2023

IEEE Trans. Med. Imaging

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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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Multi-Branch Deformable Convolutional Neural Network with Label Distribution Learning for Fetal Brain Age Prediction

Cited by 23 publications

References 9 publications

Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction

Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction

Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration

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

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