Automatic linear measurements of the fetal brain on MRI with deep neural networks

• URL: http://arxiv.org/abs/2106.08174v1
• Date: Tue, 15 Jun 2021 14:20:11 GMT
• Title: Automatic linear measurements of the fetal brain on MRI with deep neural networks
• Authors: Netanell Avisdris, Bossmat Yehuda, Ori Ben-Zvi, Daphna Link-Sourani, Liat Ben-Sira, Elka Miller, Elena Zharkov, Dafna Ben Bashat and Leo Joskowicz
• Abstract summary: The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements.
• Score: 3.48517618410801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean $L_1$ difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval ($CI_{95}$) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability. The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

Related papers

• Anatomically Constrained Tractography of the Fetal Brain [6.112565873653592]
  We advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve tractography results.
  arXiv  Detail & Related papers  (2024-03-04T19:56:19Z)
• Fuzzy Logic-Based System for Brain Tumour Detection and Classification [0.0]
  Brain Tumours (BT) are extremely dangerous and difficult to treat. Currently, doctors must manually examine images and mark out tumour regions to diagnose BT. In this study, we suggest a fuzzy logic-based system for categorising BT.
  arXiv  Detail & Related papers  (2024-01-21T01:07:00Z)
• Fetal-BET: Brain Extraction Tool for Fetal MRI [4.214523989654048]
  We build a large annotated dataset of approximately 72,000 2D fetal brain MRI images. Using this dataset, we developed and validated deep learning methods, by exploiting the power of the U-Net style architectures. Our approach leverages the rich information from multi-contrast (multi-sequence) fetal MRI data, enabling precise delineation of the fetal brain structures.
  arXiv  Detail & Related papers  (2023-10-02T18:14:23Z)
• Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes [52.242103848335354]
  We propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans.
  arXiv  Detail & Related papers  (2023-08-13T12:51:15Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction [53.122045119395594]
  Unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results. We propose deep learning for UAD in 3D brain MRI considering additional age information. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction.
  arXiv  Detail & Related papers  (2022-01-31T09:39:52Z)
• 3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI [55.97060983868787]
  We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance. We compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE.
  arXiv  Detail & Related papers  (2021-09-14T09:17:27Z)
• Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning [2.231365407061881]
  We propose a supervised deep learning method for the direct estimation of aortic diameters. Our approach makes use of a 3D+2D convolutional neural network (CNN) that takes as input a 3D scan and outputs the aortic diameter at a given location. Overall, the 3D+2D CNN achieved a mean absolute error between 2.2-2.4 mm depending on the considered aortic location.
  arXiv  Detail & Related papers  (2020-09-09T18:28:00Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)
• Bayesian convolutional neural network based MRI brain extraction on nonhuman primates [2.2182171526013774]
  Current automatic brain extraction methods demonstrate good results on human brains, but are far from satisfactory on nonhuman primates. We propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected 3D conditional random field (CRF) The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates.
  arXiv  Detail & Related papers  (2020-05-18T05:08:30Z)
• Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes [52.53375964591765]
  We propose the first fully-automated solution to segment the whole fetal head in US volumes. The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
  arXiv  Detail & Related papers  (2020-04-28T14:43:05Z)

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.