Fat-free mass derived from bioimpedance spectroscopy and computed tomography are in good agreement in patients with chronic kidney disease.

OBJECTIVE: Malnutrition is highly prevalent in patients with kidney failure. Since body weight does not reflect body composition, other methods are needed to determine muscle mass, often estimated by fat-free mass (FFM). Bioimpedance spectroscopy (BIS) is frequently used for monitoring body composition in patients with kidney failure. Unfortunately, BIS-derived lean tissue mass (LTMBIS) is not suitable for comparison with FFM cut-off values for the diagnosis of malnutrition, or for calculating dietary protein requirements. Hypothetically, FFM could be derived from BIS data (FFMBIS). This study aims to compare FFMBIS and LTMBIS with computed tomography (CT) derived FFM (FFMCT). Secondarily, we aimed to explore the impact of using different methods on calculated protein requirements. METHODS: CT scans of 60 patients with CKD stage 4-5 were analyzed at the L3 level for muscle cross-sectional area, which was converted to FFMCT. Spearman rank correlation coefficient and 95% limits of agreement (LoA) were calculated to compare FFMBIS and LTMBIS with FFMCT. Dietary protein requirements were determined based on FFMCT, FFMBIS and adjusted body weight. Deviations over 10% were considered clinically relevant. RESULTS: FFMCT correlated most strongly with FFMBIS (r=0.78, p<0.001), in males (r=0.72, p<0.001) and in females (r=0.60, p<0.001). A mean difference of -0.54 kg was found between FFMBIS and FFMCT (LoA: -14.88 to 13.7 kg, p=0.544). Between LTMBIS and FFMCT a mean difference of -12.2 kg was apparent (LoA: -28.7 to 4.2 kg, p<0.001). Using FFMCT as a reference, FFMBIS best predicted protein requirements. The mean difference between protein requirements according to FFMBIS and FFMCT was -0.7 ± 9.9 grams in males and -0.9 ± 10.9 grams in females. CONCLUSION: FFMBIS correlates well with FFMCT at a group level, but still shows large variation within individuals. As expected, large clinically relevant differences were observed in calculated protein requirements.

Optimizing carboplatin dosing by an improved prediction of carboplatin clearance using a CT-enhanced estimate of renal function.

AIMS: Carboplatin is generally dosed based on a modified Calvert formula, in which the Cockcroft-Gault-based creatinine clearance (CRCL) is used as proxy for the glomerular filtration rate (GFR). The Cockcroft-Gault formula (CG) overpredicts CRCL in patients with an aberrant body composition. The CT-enhanced estimate of RenAl FuncTion (CRAFT) was developed to compensate for this overprediction. We aimed to evaluate whether carboplatin clearance is better predicted by CRCL based on the CRAFT compared to the CG. METHODS: Data of four previously conducted trials was used. The CRAFT was divided by serum creatinine to derive CRCL. The difference between CRAFT- and CG-based CRCL was assessed by population pharmacokinetic modelling. Furthermore, the difference in calculated carboplatin dose was assessed in a heterogeneous dataset. RESULTS: In total, 108 patients were included in the analysis. Addition of the CRAFT- and CG-based CRCL as covariate on carboplatin clearance led, respectively, to an improved model fit with a 26-point drop in objective function value and a worsened model fit with an increase of 8 points. In 19 subjects with serum creatinine <50 µmol/L, the calculated carboplatin dose was 233 mg higher using the CG. CONCLUSIONS: Carboplatin clearance is better predicted by CRAFT vs. CG-based CRCL. In subjects with low serum creatinine, the calculated carboplatin dose using CG exceeds the dose using CRAFT, which might explain the need for dose capping when using the CG. Therefore, the CRAFT might be an alternative for dose capping while still dosing accurately.

Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study.

BACKGROUND: Body composition during childhood may predispose to negative health outcomes later in life. Automatic segmentation may assist in quantifying pediatric body composition in children. OBJECTIVE: To evaluate automatic segmentation for body composition on pediatric computed tomography (CT) scans and to provide normative data on muscle and fat areas throughout childhood using automatic segmentation. MATERIALS AND METHODS: In this pilot study, 537 children (ages 1-17 years) who underwent abdominal CT after high-energy trauma at a Dutch tertiary center (2002-2019) were retrospectively identified. Of these, the CT images of 493 children (66% boys) were used to establish normative data. Muscle (psoas, paraspinal and abdominal wall) and fat (subcutaneous and visceral) areas were measured at the third lumbar vertebral (L3) level by automatic segmentation. A representative subset of 52 scans was also manually segmented to evaluate the performance of automatic segmentation. RESULTS: For manually-segmented versus automatically-segmented areas (52 scans), mean Dice coefficients were high for muscle (0.87-0.90) and subcutaneous fat (0.88), but lower for visceral fat (0.60). In the control group, muscle area was comparable for both sexes until the age of 13 years, whereafter, boys developed relatively more muscle. From a young age, boys were more prone to visceral fat storage than girls. Overall, boys had significantly higher visceral-to-subcutaneous fat ratios (median 1.1 vs. 0.6, P<0.01) and girls higher fat-to-muscle ratios (median 1.0 vs. 0.7, P<0.01). CONCLUSION: Automatic segmentation of L3-level muscle and fat areas allows for accurate quantification of pediatric body composition. Using automatic segmentation, the development in muscle and fat distribution during childhood (in otherwise healthy) Dutch children was demonstrated.

Exposure-Response Analysis of Osimertinib in EGFR Mutation Positive Non-Small Cell Lung Cancer Patients in a Real-Life Setting.

BACKGROUND: Osimertinib, an irreversible inhibitor of the epidermal growth factor receptor (EGFR) is an important drug in the treatment of EGFR-mutation positive non-small cell lung cancer (NSCLC). Clinical trials with osimertinib could not demonstrate an exposure-efficacy relationship, while a relationship between exposure and toxicity has been found. In this study, we report the exposure-response relationships of osimertinib in a real-life setting. METHODS: A retrospective observational cohort study was performed, including patients receiving 40 - 80 mg osimertinib as ≥ 2 line therapy and from whom pharmacokinetic samples were collected during routine care. Trough plasma concentrations (Cmin,pred) were estimated and used as a measure of osimertinib exposure. A previously defined exploratory pharmacokinetic threshold of 166 µg/L was taken to explore the exposure-efficacy relationship. RESULTS: A total of 145 patients and 513 osimertinib plasma concentration samples were included. Median progression free survival (PFS) was 13.3 (95% confidence interval (CI):10.3 - 19.1) months and 9.3 (95% CI: 7.2 - 11.1) months for patients with Cmin,pred < 166 µg/L and Cmin,pred ≥ 166 µg/L, respectively (p = 0.03). In the multivariate analysis, a Cmin,pred < 166 µg/L resulted in a non-statistically significant hazard ratio of 1.10 (95% CI: 0.60 - 2.01; p = 77). Presence of a EGFR driver-mutation other than the exon 19 del or L858R mutations, led to a shorter PFS with a hazard ratio of 2.89 (95% CI: 1.18 - 7.08; p = 0.02). No relationship between exposure and toxicity was observed (p = 0.91). CONCLUSION: In our real-life cohort, no exposure-response relationship was observed for osimertinib in the current dosing scheme. The feasibility of a standard lower fixed dosing of osimertinib in clinical practice should be studied prospectively.

Early human brain development: insights into macroscale connectome wiring.

BACKGROUND: Early brain development is closely dictated by distinct neurobiological principles. Here, we aimed to map early trajectories of structural brain wiring in the neonatal brain. METHODS: We investigated structural connectome development in 44 newborns, including 23 preterm infants and 21 full-term neonates scanned between 29 and 45 postmenstrual weeks. Diffusion-weighted imaging data were combined with cortical segmentations derived from T2 data to construct neonatal connectome maps. RESULTS: Projection fibers interconnecting primary cortices and deep gray matter structures were noted to mature faster than connections between higher-order association cortices (fractional anisotropy (FA) F = 58.9, p < 0.001, radial diffusivity (RD) F = 28.8, p < 0.001). Neonatal FA-values resembled adult FA-values more than RD, while RD approximated the adult brain faster (F = 358.4, p < 0.001). Maturational trajectories of RD in neonatal white matter pathways revealed substantial overlap with what is known about the sequence of subcortical white matter myelination from histopathological mappings as recorded by early neuroanatomists (mean RD 68 regions r = 0.45, p = 0.008). CONCLUSION: Employing postnatal neuroimaging we reveal that early maturational trajectories of white matter pathways display discriminative developmental features of the neonatal brain network. These findings provide valuable insight into the early stages of structural connectome development.

Severe retinopathy of prematurity is associated with reduced cerebellar and brainstem volumes at term and neurodevelopmental deficits at 2 years.

BackgroundTo evaluate the association between severe retinopathy of prematurity (ROP), measures of brain morphology at term-equivalent age (TEA), and neurodevelopmental outcome.MethodsEighteen infants with severe ROP (median gestational age (GA) 25.3 (range 24.6-25.9 weeks) were included in this retrospective case-control study. Each infant was matched to two extremely preterm control infants (n=36) by GA, birth weight, sex, and brain injury. T2-weighted images were obtained on a 3 T magnetic resonance imaging (MRI) at TEA. Brain volumes were computed using an automatic segmentation method. In addition, cortical folding metrics were extracted. Neurodevelopment was formally assessed at the ages of 15 and 24 months.ResultsInfants with severe ROP had smaller cerebellar volumes (21.4±3.2 vs. 23.1±2.6 ml; P=0.04) and brainstem volumes (5.4±0.5 ml vs. 5.8±0.5 ml; P=0.01) compared with matched control infants. Furthermore, ROP patients showed a significantly lower development quotient (Griffiths Mental Development Scales) at the age of 15 months (93±15 vs. 102±10; P=0.01) and lower fine motor scores (10±3 vs. 12±2; P=0.02) on Bayley Scales (Third Edition) at the age of 24 months.ConclusionSevere ROP was associated with smaller volumes of the cerebellum and brainstem and with poorer early neurodevelopmental outcome. Follow-up through childhood is needed to evaluate the long-term consequences of our findings.

Changes in brain morphology and microstructure in relation to early brain activity in extremely preterm infants.

Background and ObjectiveTo investigate the relation of early brain activity with structural (growth of the cortex and cerebellum) and white matter microstructural brain development.MethodsA total of 33 preterm neonates (gestational age 26±1 weeks) without major brain abnormalities were continuously monitored with electroencephalography during the first 48 h of life. Rate of spontaneous activity transients per minute (SAT rate) and inter-SAT interval (ISI) in seconds per minute were calculated. Infants underwent brain magnetic resonance imaging ∼30 (mean 30.5; min: 29.3-max: 32.0) and 40 (41.1; 40.0-41.8) weeks of postmenstrual age. Increase in cerebellar volume, cortical gray matter volume, gyrification index, fractional anisotropy (FA) of posterior limb of the internal capsule, and corpus callosum (CC) were measured.ResultsSAT rate was positively associated with cerebellar growth (P=0.01), volumetric growth of the cortex (P=0.027), increase in gyrification (P=0.043), and increase in FA of the CC (P=0.037). ISI was negatively associated with cerebellar growth (P=0.002).ConclusionsIncreased early brain activity is associated with cerebellar and cortical growth structures with rapid development during preterm life. Higher brain activity is related to FA microstructural changes in the CC, a region responsible for interhemispheric connections. This study underlines the importance of brain activity for microstructural brain development.

Relation between clinical risk factors, early cortical changes, and neurodevelopmental outcome in preterm infants.

Cortical folding mainly takes place in the third trimester of pregnancy and may therefore be influenced by preterm birth. The aim of this study was to evaluate the development of specific cortical structures between early age (around 30weeks postmenstrual age) and term-equivalent age (TEA, around 40weeks postmenstrual age) in 71 extremely preterm infants, and to associate this to clinical characteristics and neurodevelopmental outcome at two years of age. First, analysis showed that the central sulcus (CS), lateral fissure (LF) and insula (INS) were present at early MRI in all infants, whereas the other sulci (post-central sulcus [PCS], superior temporal sulcus [STS], superior [SFS] and inferior [IFS] frontal sulcus) were only seen in part of the infants. Relative growth from early to TEA examination was largest in the SFS. A rightward asymmetry of the surface area was seen in development between both examinations except for the LF, which showed a leftward asymmetry at both time points. Second, lower birth weight z-score, multiple pregnancy and prolonged mechanical ventilation showed negative effects on cortical folding of the CS, LF, INS, STS and PCS, mainly on the first examination, suggesting that sulci developing the earliest were the most affected by clinical factors. Finally, in this cohort, a clear association between cortical folding and neurodevelopmental outcome at two years corrected age was found, particularly for receptive language.

Delayed cortical gray matter development in neonates with severe congenital heart disease.

BACKGROUND: This study aimed to assess cortical gray matter growth and maturation in neonates with congenital heart disease (CHD). METHODS: Thirty-one (near) term neonates with severe CHD (8 univentricular heart malformation (UVH), 21 d-transposition of great arteries (d-TGA) and 2 aortic coarctation) underwent cerebral MRI before (postnatal-day 7) and after (postnatal-day 24) surgery. Eighteen controls with similar gestational age had one MRI (postnatal-day 23). Cortical gray matter volume (CGM), inner cortical surface (iCS), and median cortical thickness were extracted as measures of volumetric growth, and gyrification index (GI) as measure of maturation. RESULTS: Over a median of 18 d, CGM increased by 21%, iCS by 17%, thickness and GI both by 9%. Decreased postoperative CGM and iCS were seen for CHD compared to controls (P values < 0.01), however with similar thickness and GI. UVH showed lower postoperative iCS, thickness (P values < 0.05) and GI (P value < 0.01) than d-TGA and controls. Infants requiring preoperative balloon-atrioseptostomy (BAS, 61%) had reduced postoperative CGM, iCS, and GI (P values < 0.05). CONCLUSION: Infants with severe CHD show reduced cortical volumes compared to controls with gyrification being delayed in UVH, but not in d-TGA. Infants requiring BAS show higher risk of impaired cortical volume and gyrification.

Automatic segmentation of MR brain images of preterm infants using supervised classification.

Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data from the partially annotated images resulted in slightly lower Dice coefficients, the performance in all experiments was close to that of a second human expert (0.93 for WM, 0.79 for GM and 0.86 for CSF for the images acquired at 30weeks, and 0.94 for WM, 0.76 for GM and 0.87 for CSF for the images acquired at 40weeks). These results show that the presented method is robust to age and acquisition protocol and that it performs accurate segmentation of WM, GM, and CSF when the training data is extracted from complete annotations as well as when the training data is extracted from partial annotations only. This extends the applicability of the method by reducing the time and effort necessary to create training data in a population with different characteristics.

Low muscle quality on a procedural computed tomography scan assessed with deep learning as a practical useful predictor of mortality in patients with severe aortic valve stenosis.

BACKGROUND & AIMS: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. METHODS: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. RESULTS: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15-1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01-1.52], p = 0.04). CONCLUSIONS: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.

The association of radiologic body composition parameters with clinical outcomes in level-1 trauma patients.

PURPOSE: The present study aims to assess whether CT-derived muscle mass, muscle density, and visceral fat mass are associated with in-hospital complications and clinical outcome in level-1 trauma patients. METHODS: A retrospective cohort study was conducted on adult patients admitted to the University Medical Center Utrecht following a trauma between January 1 and December 31, 2017. Trauma patients aged 16 years or older without severe neurological injuries, who underwent a CT that included the abdomen within 7 days of admission, were included. An artificial intelligence (AI) algorithm was used to retrieve muscle areas to calculate the psoas muscle index and to retrieve psoas muscle radiation attenuation and visceral fat (VF) area from axial CT images. Multivariable logistic and linear regression analyses were performed to assess associations between body composition parameters and outcomes. RESULTS: A total of 404 patients were included for analysis. The median age was 49 years (interquartile range [IQR] 30-64), and 66.6% were male. Severe comorbidities (ASA 3-4) were seen in 10.9%, and the median ISS was 9 (IQR 5-14). Psoas muscle index was not independently associated with complications, but it was associated with ICU admission (odds ratio [OR] 0.79, 95% confidence interval [CI] 0.65-0.95), and an unfavorable Glasgow Outcome Scale (GOS) score at discharge (OR 0.62, 95% CI 0.45-0.85). Psoas muscle radiation attenuation was independently associated with the development of any complication (OR 0.60, 95% CI 0.42-0.85), pneumonia (OR 0.63, 95% CI 0.41-0.96), and delirium (OR 0.49, 95% CI 0.28-0.87). VF was associated with developing a delirium (OR 1.95, 95% CI 1.12-3.41). CONCLUSION: In level-1 trauma patients without severe neurological injuries, automatically derived body composition parameters are able to independently predict an increased risk of specific complications and other poor outcomes.

The Association between Muscle Quantity and Overall Survival Depends on Muscle Radiodensity: A Cohort Study in Non-Small-Cell Lung Cancer Patients.

The prognostic value of CT-derived muscle quantity for overall survival (OS) in patients with non-small-cell lung cancer (NSCLC) is uncertain due to conflicting evidence. We hypothesize that increased muscle quantity is associated with better OS in patients with normal muscle radiodensity but not in patients with fatty degeneration of muscle tissue and low muscle radiodensity. We performed an observational cohort study in NSCLC patients treated with radiotherapy. A deep learning algorithm was used to measure muscle quantity as psoas muscle index (PMI) and psoas muscle radiodensity (PMD) on computed tomography. The potential interaction between PMI and PMD for OS was investigated using Cox proportional-hazards regression. Baseline adjustment variables were age, sex, histology, performance score and body mass index. We investigated non-linear effects of continuous variables and imputed missing values using multiple imputation. We included 2840 patients and observed 1975 deaths in 5903 patient years. The average age was 68.9 years (standard deviation 10.4, range 32 to 96) and 1692 patients (59.6%) were male. PMI was more positively associated with OS for higher values of PMD (hazard ratio for interaction 0.915; 95% confidence interval 0.861-0.972; p-value 0.004). We found evidence that high muscle quantity is associated with better OS when muscle radiodensity is higher, in a large cohort of NSCLC patients treated with radiotherapy. Future studies on the association between muscle status and OS should accommodate this interaction in their analysis for more accurate and more generalizable results.

Evaluation of a Fully Automatic Deep Learning-Based Method for the Measurement of Psoas Muscle Area.

Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method. Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap). Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2. Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.

Towards Personalised Contrast Injection: Artificial-Intelligence-Derived Body Composition and Liver Enhancement in Computed Tomography.

In contrast-enhanced computed tomography, total body weight adapted contrast injection protocols have proven successful in achieving a homogeneous enhancement of vascular structures and liver parenchyma. However, because solid organs have greater perfusion than adipose tissue, the lean body weight (fat-free mass) rather than the total body weight is theorised to cause even more homogeneous enhancement. We included 102 consecutive patients who underwent a multiphase abdominal computed tomography between March 2016 and October 2019. Patients received contrast media (300 mgI/mL) according to bodyweight categories. Using regions of interest, we measured the Hounsfield unit (HU) increase in liver attenuation from unenhanced to contrast-enhanced computed tomography. Furthermore, subjective image quality was graded using a four-point Likert scale. An artificial intelligence algorithm automatically segmented and determined the body compositions and calculated the percentages of lean body weight. The hepatic enhancements were adjusted for iodine dose and iodine dose per total body weight, as well as percentage lean body weight. The associations between enhancement and total body weight, body mass index, and lean body weight were analysed using linear regression. Patients had a median age of 68 years (IQR: 58-74), a total body weight of 81 kg (IQR: 73 - 90), a body mass index of 26 kg/m2 (SD: ±4.2), and a lean body weight percentage of 50% (IQR: 36 - 55). Mean liver enhancements in the portal venous phase were 61 ± 12 HU (≤ 70 kg), 53 ± 10 HU (70 - 90 kg), and 53 ± 7 HU (≥ 90 kg). The majority (93%) of scans were rated as good or excellent. Regression analysis showed significant correlations between liver enhancement corrected for injected total iodine and total body weight (r = 0.53; p < 0.001) and between liver enhancement corrected for lean body weight and the percentage of lean body weight (r = 0.73; p < 0.001). Most benefits from personalising iodine injection using %LBW additive to total body weight would be achieved in patients under 90 kg. Liver enhancement is more strongly associated with the percentage of lean body weight than with the total body weight or body mass index. The observed variation in liver enhancement might be reduced by a personalised injection based on the artificial-intelligence-determined percentage of lean body weight.

Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge.

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T1-weighted image, a T2-weighted fluid attenuated inversion recovery (FLAIR) image and a T1-weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge (n = 20), quantitatively and qualitatively in relatively healthy older subjects (n = 96), and qualitatively in patients from a memory clinic (n = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman's ρ = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts.

Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images.

This study investigates the predictive ability of automatic quantitative brain MRI descriptors for the identification of infants with low cognitive and/or motor outcome at 2-3 years chronological age. MR brain images of 173 patients were acquired at 30 weeks postmenstrual age (PMA) (n = 86) and 40 weeks PMA (n = 153) between 2008 and 2013. Eight tissue volumes and measures of cortical morphology were automatically computed. A support vector machine classifier was employed to identify infants who exhibit low cognitive and/or motor outcome (<85) at 2-3 years chronological age as assessed by the Bayley scales. Based on the images acquired at 30 weeks PMA, the automatic identification resulted in an area under the receiver operation characteristic curve (AUC) of 0.78 for low cognitive outcome, and an AUC of 0.80 for low motor outcome. Identification based on the change of the descriptors between 30 and 40 weeks PMA (n = 66) resulted in an AUC of 0.80 for low cognitive outcome and an AUC of 0.85 for low motor outcome. This study provides evidence of the feasibility of identification of preterm infants at risk of cognitive and motor impairments based on descriptors automatically computed from images acquired at 30 and 40 weeks PMA.

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network.

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2-weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

Development of Cortical Morphology Evaluated with Longitudinal MR Brain Images of Preterm Infants.

INTRODUCTION: The cerebral cortex develops rapidly in the last trimester of pregnancy. In preterm infants, brain development is very vulnerable because of their often complicated extra-uterine conditions. The aim of this study was to quantitatively describe cortical development in a cohort of 85 preterm infants with and without brain injury imaged at 30 and 40 weeks postmenstrual age (PMA). METHODS: In the acquired T2-weighted MR images, unmyelinated white matter (UWM), cortical grey matter (CoGM), and cerebrospinal fluid in the extracerebral space (CSF) were automatically segmented. Based on these segmentations, cortical descriptors evaluating volume, surface area, thickness, gyrification index, and global mean curvature were computed at both time points, for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes separately. Additionally, visual scoring of brain abnormality was performed using a conventional scoring system at 40 weeks PMA. RESULTS: The evaluated descriptors showed larger change in the occipital lobes than in the other lobes. Moreover, the cortical descriptors showed an association with the abnormality scores: gyrification index and global mean curvature decreased, whereas, interestingly, median cortical thickness increased with increasing abnormality score. This was more pronounced at 40 weeks PMA than at 30 weeks PMA, suggesting that the period between 30 and 40 weeks PMA might provide a window of opportunity for intervention to prevent delay in cortical development.