Long-term neuropathological and/or neurobehavioral effects of antenatal corticosteroid therapy in animal models: a systematic review

BACKGROUND: Antenatal corticosteroids (ACSs) are recommended to all women at risk for preterm delivery; currently, there is controversy about the subsequent long-term neurocognitive sequelae. This systematic review summarizes the long-term neurodevelopmental outcomes after ACS therapy in animal models. METHODS: An electronic search strategy incorporating MeSH and keywords was performed using all known literature databases and in accordance with PRISMA guidance (PROSPERO CRD42019119663). RESULTS: Of the 669 studies identified, eventually 64 were included. The majority of studies utilized dexamethasone at relative high dosages and primarily involved rodents. There was a high risk of bias, mostly due to lack of randomization, allocation concealment, and blinding. The main outcomes reported on was neuropathological, particularly glucocorticoid receptor expression and neuron densities, and neurobehavior. Overall there was an upregulation of glucocorticoid receptors with lower neuron densities and a dysregulation of the dopaminergic and serotonergic systems. This coincided with various adverse neurobehavioral outcomes. CONCLUSIONS: In animal models, ACSs consistently lead to deleterious long-term neurocognitive effects. This may be due to the specific agents, i.e., dexamethasone, or the repetitive/higher total dosing used. ACS administration varied significantly between studies and there was a high risk of bias. Future research should be standardized in well-characterized models

Fetal surgery for spina bifida

Open spina bifida is often diagnosed during pregnancy. In the last 20 years, fetal repair has been shown to have benefits to the neonate and child, and is now widely available, including within the UK. This article briefly examines the background, evidence, benefits and risks of fetal surgery for spina bifida

Bruker2nifti: Magnetic Resonance Images converter from Bruker ParaVision to Nifti format

In clinical and pre-clinical research involving medical images, the first step following a Magnetic Resonance Imaging dataset acquisition, usually entails the conversion of image data from the native scanner format to a format suitable for the intended analysis. The proprietary [Bruker ParaVision](https://www.bruker.com/products/mr/preclinical-mri/software/service-support.html) software currently does not provide the tools for conversion of the data to suitable and open formats for research, such as nifti [@cox2004sort], for which most of the available tools for medical image analysis are implemented. For this purpose we have designed and developed [bruker2nifti](https://github.com/SebastianoF/bruker2nifti), a pip-installable Python tool provided with a Graphical User Interface to convert from the native MRI Bruker format to the nifti format, without any intermediate step through the DICOM standard formats [@Mildenberger2002]. Bruker2nifti is intended to be a tool to access the data structure and to parse all parameter files of the Bruker ParaVision format into python dictionaries, to select the relevant information to fill the Nifti header and data volume. Lastly it is meant to be a starting point where to integrate possible future variations in Bruker hardware and ParaVision software future releases

Label-Set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

Deep neural networks have increased the accuracy of automatic segmentation, however their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not all, regions of interest are segmented are necessary to make better use of partially annotated datasets. In this paper, we propose the first axiomatic definition of label-set loss functions that are the loss functions that can handle partially segmented images. We prove that there is one and only one method to convert a classical loss function for fully segmented images into a proper label-set loss function. Our theory also allows us to define the leaf-Dice loss, a label-set generalisation of the Dice loss particularly suited for partial supervision with only missing labels. Using the leaf-Dice loss, we set a new state of the art in partially supervised learning for fetal brain 3D MRI segmentation. We achieve a deep neural network able to segment white matter, ventricles, cerebellum, extra-ventricular CSF, cortical gray matter, deep gray matter, brainstem, and corpus callosum based on fetal brain 3D MRI of anatomically normal fetuses or with open spina bifida. Our implementation of the proposed label-set loss functions is available at https://github.com/LucasFidon/label-set-loss-functions

Label-Set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

Deep neural networks have increased the accuracy of automatic segmentation, however their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not all, regions of interest are segmented are necessary to make better use of partially annotated datasets. In this paper, we propose the first axiomatic definition of label-set loss functions that are the loss functions that can handle partially segmented images. We prove that there is one and only one method to convert a classical loss function for fully segmented images into a proper label-set loss function. Our theory also allows us to define the leaf-Dice loss, a label-set generalisation of the Dice loss particularly suited for partial supervision with only missing labels. Using the leaf-Dice loss, we set a new state of the art in partially supervised learning for fetal brain 3D MRI segmentation. We achieve a deep neural network able to segment white matter, ventricles, cerebellum, extra-ventricular CSF, cortical gray matter, deep gray matter, brainstem, and corpus callosum based on fetal brain 3D MRI of anatomically normal fetuses or with open spina bifida. Our implementation of the proposed label-set loss functions is available at https://github.com/LucasFidon/label-set-loss-functions

Engineering handbook

2005 handbook for the faculty of Engineerin

Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 ∘ and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment

Globally Optimal Fetoscopic Mosaicking Based on Pose Graph Optimisation With Affine Constraints

Fetoscopic laser ablation surgery could be guided using a high-quality panorama of the operating site, representing a map of the placental vasculature. This can be achieved during the initial inspection phase of the procedure using image mosaicking techniques. Due to the lack of camera calibration in the operating room, it has been mostly modelled as an affine registration problem. While previous work mostly focuses on image feature extraction for visual odometry, the challenges related to large-scale reconstruction (re-localisation, loop closure, drift correction) remain largely unaddressed in this context. This letter proposes using pose graph optimisation to produce globally optimal image mosaics of placental vessels. Our approach follows the SLAM framework with a front-end for visual odometry and a back-end for long-term refinement. Our front-end uses a recent state-of-the-art odometry approach based on vessel segmentation, which is then managed by a key-frame structure and the bag-of-words (BoW) scheme to retrieve loop closures. The back-end, which is our key contribution, models odometry and loop closure constraints as a pose graph with affine warpings between states. This problem in the special Euclidean space cannot be solved by existing pose graph algorithms and available libraries such as G2O. We model states on affine Lie group with local linearisation in its Lie algebra. The cost function is established using Mahalanobis distance with the vectorisation of the Lie algebra. Finally, an iterative optimisation algorithm is adopted to minimise the cost function. The proposed pose graph optimisation is first validated on simulation data with a synthetic trajectory that has different levels of noise and different numbers of loop closures. Then the whole system is validated using real fetoscopic data that has three sequences with different numbers of frames and loop closures. Experimental results validate the advantage of the proposed method compared with baselines