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

DaT-SPECT assessment depicts dopamine depletion among asymptomatic G2019S LRRK2 mutation carriers

Identification of early changes in Dopamine-Transporter (DaT) SPECT imaging expected in the prodromal phase of Parkinson’s disease (PD), are usually overlooked. Carriers of the G2019S LRRK2 mutation are known to be at high risk for developing PD, compared to non-carriers. In this work we aimed to study early changes in Dopamine uptake in non-manifesting PD carriers (NMC) of the G2019S LRRK2 mutation using quantitative DaT-SPECT analysis and to examine the potential for early prediction of PD. Eighty Ashkenazi-Jewish subjects were included in this study: eighteen patients with PD; thirty-one NMC and thirty-one non-manifesting non-carriers (NMNC). All subjects underwent a through clinical assessment including evaluation of motor, olfactory, affective and non-motor symptoms and DaT-SPECT imaging. A population based DaT-SPECT template was created based on the NMNC cohort, and data driven volumes-of-interest (VOIs) were defined. Comparisons between groups were performed based on VOIs and voxel-wise analysis. The striatum area of all three cohorts was segmented into four VOIs, corresponding to the right/left dorsal and ventral striatum. Significant differences in clinical measures were found between patients with PD and non-manifesting subjects with no differences between NMC and NMNC. Significantly lower uptake (p<0.001) was detected in the right and left dorsal striatum in the PD group (2.2±0.3, 2.3±0.4) compared to the NMC (4.2±0.6, 4.3±0.5) and NMNC (4.5±0.6, 4.6±0.6), and significantly (p = 0.05) lower uptake in the right dorsal striatum in the NMC group compared to NMNC. Converging results were obtained using voxel-wise analysis. Two NMC participants, who later phenoconverted into PD, demonstrated reduced uptake mainly in the dorsal striatum. No significant correlations were found between the DaT-SPECT uptake in the different VOIs and clinical and behavioral assessments in the non-manifesting groups. This study shows the clinical value of quantitative assessment of DaT-SPECT imaging and the potential for predicting PD by detection of dopamine depletion, already at the pre-symptomatic stage

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

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

DUSTER: dynamic contrast enhance up-sampled temporal resolution analysis method

Dynamic contrast enhanced (DCE) MRI using Tofts\u27 model for estimating vascular permeability is widely accepted, yet inter-tissue differences in bolus arrival time (BAT) are generally ignored. In this work we propose a method, incorporating the BAT in the analysis, demonstrating its applicability and advantages in healthy subjects and patients. A method for DCE Up Sampled TEmporal Resolution (DUSTER) analysis is proposed which includes: baseline T1 map using DESPOT1 analyzed with flip angle (FA) correction; preprocessing; raw-signal-to-T1-to-concentration time curves (CTC) conversion; automatic arterial input function (AIF) extraction at temporal super-resolution; model fitting with model selection while incorporating BAT in the pharmacokinetic (PK) model, and fits contrast agent CTC while using exhaustive search in the BAT dimension in super-resolution. The method was applied to simulated data and to human data from 17 healthy subjects, six patients with glioblastoma, and two patients following stroke. BAT values were compared to time-to-peak (TTP) values extracted from dynamic susceptibility contrast imaging. Results show that the method improved the AIF estimation and allowed extraction of the BAT with a resolution of 0.8 s. In simulations, lower mean relative errors were detected for all PK parameters extracted using DUSTER compared to analysis without BAT correction (vp:5% vs. 20%, Ktrans: 9% vs. 24% and Kep: 8% vs. 17%, respectively), and BAT estimates demonstrated high correlations (r = 0.94, p \u3c 1e− 10) with true values. In real data, high correlations between BAT values were detected when extracted from data acquired with high temporal resolution (2 s) and sub-sampled standard resolution data (6 s) (mean r = 0.85,p \u3c 1e− 10). BAT and TTP values were significantly correlated in the different brain regions in healthy subjects (mean r = 0.72,p = \u3c 1e− 3), as were voxel-wise comparisons in patients (mean r = 0.89, p \u3c 1e− 10). In conclusion, incorporating BAT in DCE analysis improves estimation accuracy for the AIF and the PK parameters while providing an additional clinically important parameter