3 research outputs found
Automatic linear measurements of the fetal brain on MRI with deep neural networks
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 difference of 1.55mm, 1.45mm and 1.23mm
respectively, and a Bland-Altman 95% confidence interval () 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.Comment: 15 pages, 8 figures, presented in CARS 2020, submitted to IJCAR
Fetal brain tissue annotation and segmentation challenge results
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero
Fetal Brain Tissue Annotation and Segmentation Challenge Results
In-utero fetal MRI is emerging as an important tool in the diagnosis and
analysis of the developing human brain. Automatic segmentation of the
developing fetal brain is a vital step in the quantitative analysis of prenatal
neurodevelopment both in the research and clinical context. However, manual
segmentation of cerebral structures is time-consuming and prone to error and
inter-observer variability. Therefore, we organized the Fetal Tissue Annotation
(FeTA) Challenge in 2021 in order to encourage the development of automatic
segmentation algorithms on an international level. The challenge utilized FeTA
Dataset, an open dataset of fetal brain MRI reconstructions segmented into
seven different tissues (external cerebrospinal fluid, grey matter, white
matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international
teams participated in this challenge, submitting a total of 21 algorithms for
evaluation. In this paper, we provide a detailed analysis of the results from
both a technical and clinical perspective. All participants relied on deep
learning methods, mainly U-Nets, with some variability present in the network
architecture, optimization, and image pre- and post-processing. The majority of
teams used existing medical imaging deep learning frameworks. The main
differences between the submissions were the fine tuning done during training,
and the specific pre- and post-processing steps performed. The challenge
results showed that almost all submissions performed similarly. Four of the top
five teams used ensemble learning methods. However, one team's algorithm
performed significantly superior to the other submissions, and consisted of an
asymmetrical U-Net network architecture. This paper provides a first of its
kind benchmark for future automatic multi-tissue segmentation algorithms for
the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript
submitte