Test-time augmentation-based active learning and self-training for label-efficient segmentation

Deep learning techniques depend on large datasets whose annotation is time-consuming. To reduce annotation burden, the self-training (ST) and active-learning (AL) methods have been developed as well as methods that combine them in an iterative fashion. However, it remains unclear when each method is the most useful, and when it is advantageous to combine them. In this paper, we propose a new method that combines ST with AL using Test-Time Augmentations (TTA). First, TTA is performed on an initial teacher network. Then, cases for annotation are selected based on the lowest estimated Dice score. Cases with high estimated scores are used as soft pseudo-labels for ST. The selected annotated cases are trained with existing annotated cases and ST cases with border slices annotations. We demonstrate the method on MRI fetal body and placenta segmentation tasks with different data variability characteristics. Our results indicate that ST is highly effective for both tasks, boosting performance for in-distribution (ID) and out-of-distribution (OOD) data. However, while self-training improved the performance of single-sequence fetal body segmentation when combined with AL, it slightly deteriorated performance of multi-sequence placenta segmentation on ID data. AL was helpful for the high variability placenta data, but did not improve upon random selection for the single-sequence body data. For fetal body segmentation sequence transfer, combining AL with ST following ST iteration yielded a Dice of 0.961 with only 6 original scans and 2 new sequence scans. Results using only 15 high-variability placenta cases were similar to those using 50 cases. Code is available at: https://github.com/Bella31/TTA-quality-estimation-ST-ALComment: Accepted to MICCAI MILLanD workshop 202

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 $L_1$ 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.Comment: 15 pages, 8 figures, presented in CARS 2020, submitted to IJCAR

Radiologically isolated aquaporin-4 antibody neuromyelitis optica spectrum disorder

Aquaporin-4 antibody (AQP4-Ab) Neuromyelitis Optica Spectrum Disorder (NMOSD) is a rare neuroinflammatory syndrome presenting predominantly with optic neuritis and transverse myelitis. We report a case of radiologically isolated longitudinally extensive optic neuritis in an asymptomatic 12-year-old female with positive serum AQP4-Ab, with resolution of imaging changes after immune therapy. By contrast to patients with radiologically isolated syndrome, of which some will never convert to multiple sclerosis, the pathogenicity of AQP4-Ab in the context of sub-clinical disease, supported treatment in our patient. Given the severe morbidity in AQP4-Ab NMOSD, prognostic biomarkers for disease severity are required to guide optimal therapy for patients

Brain Diffusivity in Infants With Hypoxic-Ischemic Encephalopathy Following Whole Body Hypothermia: Preliminary Results

Abstract Hypoxic-ischemic encephalopathy is an important cause of neuropsychological deficits. Little is known about brain diffusivity in these infants following cooling and its potential in predicting outcome. Diffusion tensor imaging was applied to 3 groups: (1) three infants with hypoxic-ischemic encephalopathy: cooled; (2) three infants with hypoxic-ischemic encephalopathy: noncooled; and (3) four controls. Diffusivity values at the corticospinal tract, thalamus, and putamen were correlated with Apgar scores and early neurodevelopmental outcome. While cooled infants exhibited lower Apgar scores than noncooled infants, their developmental scores at a mean age of 8 months were higher. All groups differed in their diffusivity values with the cooled infants showing better values compared with the noncooled, correlating with early neurodevelopmental outcome. These preliminary results indicate that diffusion tensor imaging performed at an early age in infants with hypoxic-ischemic encephalopathy may forecast clinical outcome and support the neuroprotective effect of hypothermia treatment

A randomized, placebo-controlled trial of prednisone in early Henoch Schönlein Purpura [ISRCTN85109383]

BACKGROUND: Henoch Schönlein Purpura (HSP) is the most common systemic vasculitis of childhood. There is considerable controversy over whether children with HSP should be treated with corticosteroids. The goal of this study was to investigate whether early corticosteroid administration could reduce the rate of renal or gastrointestinal complications in children with HSP. METHODS: Forty children with HSP, seen in the emergency room of a tertiary-care, paediatric centre, entered a randomized, double-blind, placebo controlled study. The treatment group (n = 21) received oral prednisone, 2 mg/kg/day for one week, with weaning over a second week, while the placebo group (n = 19) received an identical appearing placebo. Co-primary outcomes were the rate of renal involvement at one year and the rate of acute gastrointestinal complications. Co-primary outcomes were analysed using Fisher's Exact test. RESULTS: At one year, there was no difference in the rate of renal involvement (3/21 prednisone group vs. 2/19 placebo group, P = 1.0). There was also no statistically significant difference in the rate of acute gastrointestinal complications (2/21 prednisone group vs. 3/19 placebo group, P = 0.7). Two children in the placebo group did experience intussusceptions compared with none in the prednisone group (P = 0.2). CONCLUSIONS: Early prednisone therapy in HSP does not appear to reduce the risk of renal involvement at one year, or the risk of acute gastrointestinal complications. There may be a reduced risk of intussusception. The routine, early use of prednisone in uncomplicated HSP cannot be recommended at this time

Clinical benefits of diffusion tensor imaging in hydrocephalus

OBJECT The object of this study was to use diffusion tensor imaging (DTI) to evaluate and characterize white matter changes in hydrocephalus. METHODS The authors performed a retrospective analysis of DTI in a cohort of patients with hydrocephalus (n = 35), 19 of whom had both pre- and postsurgical imaging studies. These patient's DTI values were compared with values extracted from age-dependent trend lines computed from a healthy subject group (n = 70, age span 14 months-14 years). Several DTI parameters in different regions of interest (ROIs) were evaluated to find the most sensitive parameters for clinical decision making in hydrocephalus. RESULTS Compared with healthy controls, patients with active hydrocephalus had a statistically significant change in all DTI parameters. The most sensitive and specific DTI parameter for predicting hydrocephalus was axial diffusivity (λ1) measured at the level of the corona radiata. Diffusion tensor imaging parameters correlated with several conventional radiological parameters in the assessment of hydrocephalus but were not superior to them. There was no convincing correlation between clinical disease severity and DTI parameters. When examining the pre- and postsurgical effect, it was found that DTI may be a sensitive tool for estimating tissue improvement. CONCLUSIONS This large-cohort study with a multidisciplinary approach combining clinical, neurological, radiological, and multiple DTI parameters revealed the most sensitive DTI parameters for identifying hydrocephalus and suggested that they may serve as an important tool for the disorder's quantitative radiological assessment

Fetal and Perinatal Outcome Following First and Second Trimester COVID-19 Infection: Evidence from a Prospective Cohort Study

A novel coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus causing coronavirus disease 2019 (COVID-19) disease, which emerged as a global pandemic. Data regarding the implications of COVID-19 disease at early gestation on fetal and obstetric outcomes is scarce. Thus, our aim was to investigate the effect of first and second trimester maternal COVID-19 disease on fetal and perinatal outcomes. This was a prospective cohort study of pregnant women with a laboratory-proven SARS-COV-2 infection contracted prior to 26 weeks gestation. Women were followed at a single tertiary medical center by serial sonographic examinations every 4–6 weeks to assess fetal well-being, growth, placental function, anatomic evaluation and signs of fetal infection. Amniocentesis was offered to assess amniotic fluid SARS-COV-2-PCR (polymerase chain reaction) and fetal brain magnetic resonance imaging (MRI) was offered at 30–32 weeks gestation. Demographic, obstetric and neonatal data were collected from history intake, medical charts or by telephone survey. Perinatal outcomes were compared between women infected at first vs. second trimester. 55 women with documented COVID-19 disease at early gestation were included and followed at our center. The mean maternal age was 29.6 ± 6.2 years and the mean gestational age at viral infection was 14.2 ± 6.7 weeks with 28 (51%) women infected at the first trimester and 27 (49%) at the second trimester. All patients but one experienced asymptomatic to mild symptoms. Of 22 patients who underwent amniocentesis, none had evidence of vertical transmission. None of the fetuses exhibited signs of central nervous system (CNS) disease, growth restriction and placental dysfunction on serial ultrasound examinations and fetal MRI. Pregnancies resulted in perinatal survival of 100% to date with mean gestational age at delivery of 38.6 ± 3.0 weeks and preterm birth <37 weeks rate of 3.4%. The mean birthweight was 3260 ± 411 g with no cases of small for gestational age infants. The obstetric and neonatal outcomes were similar among first vs. second trimester infection groups. We conclude SARS-CoV-2 infection at early gestation was not associated with vertical transmission and resulted in favorable obstetric and neonatal outcomes