Convolutional neural networks for the segmentation of small rodent brain MRI

Image segmentation is a common step in the analysis of preclinical brain MRI, often performed manually. This is a time-consuming procedure subject to inter- and intra- rater variability. A possible alternative is the use of automated, registration-based segmentation, which suffers from a bias owed to the limited capacity of registration to adapt to pathological conditions such as Traumatic Brain Injury (TBI). In this work a novel method is developed for the segmentation of small rodent brain MRI based on Convolutional Neural Networks (CNNs). The experiments here presented show how CNNs provide a fast, robust and accurate alternative to both manual and registration-based methods. This is demonstrated by accurately segmenting three large datasets of MRI scans of healthy and Huntington disease model mice, as well as TBI rats. MU-Net and MU-Net-R, the CCNs here presented, achieve human-level accuracy while eliminating intra-rater variability, alleviating the biases of registration-based segmentation, and with an inference time of less than one second per scan. Using these segmentation masks I designed a geometric construction to extract 39 parameters describing the position and orientation of the hippocampus, and later used them to classify epileptic vs. non-epileptic rats with a balanced accuracy of 0.80, five months after TBI. This clinically transferable geometric approach detects subjects at high-risk of post-traumatic epilepsy, paving the way towards subject stratification for antiepileptogenesis studies

Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks

We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-field correction or registration to a template, produces segmentations in less than a second, and its GPU memory requirements can be adjusted based on the available resources. We optimized MedicDeepLabv3+ and six other state-of-the-art convolutional neural networks (DeepLabv3+, UNet, HighRes3DNet, V-Net, VoxResNet, Demon) on a heterogeneous training set comprised by MR volumes from 11 cohorts acquired at different lesion stages. Then, we evaluated the trained models and two approaches specifically designed for rodent MRI skull stripping (RATS and RBET) on a large dataset of 655 MR rat brain volumes. In our experiments, MedicDeepLabv3+ outperformed the other methods, yielding an average Dice coefficient of 0.952 and 0.944 in the brain and contralateral hemisphere regions. Additionally, we show that despite limiting the GPU memory and the training data, our MedicDeepLabv3+ also provided satisfactory segmentations. In conclusion, our method, publicly available at https://github.com/jmlipman/MedicDeepLabv3Plus, yielded excellent results in multiple scenarios, demonstrating its capability to reduce human workload in rat neuroimaging studies.Comment: Published in NeuroInformatic

RatLesNetv2: a fully convolutional network for rodent brain lesion segmentation

We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat brain MRI scans of 671 rats at nine different lesion stages that were used to study focal cerebral ischemia for drug development. In addition, we compared its performance with three other ConvNets specifically designed for medical image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient values than the other ConvNets and it produced much more realistic and compact segmentations with notably fewer holes and lower Hausdorff distance. The Dice scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of manual segmentations. In conclusion, RatLesNetv2 could be used for automated lesion segmentation, reducing human workload and improving reproducibility. RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2

Hippocampal position and orientation as prognostic biomarkers for posttraumatic epileptogenesis: an experimental study in a rat lateral fluid percussion model

Objective This study was undertaken to identify prognostic biomarkers for posttraumatic epileptogenesis derived from parameters related to the hippocampal position and orientation. Methods Data were derived from two preclinical magnetic resonance imaging (MRI) follow-up studies: EPITARGET (156 rats) and Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx; University of Eastern Finland cohort, 43 rats). Epileptogenesis was induced with lateral fluid percussion-induced traumatic brain injury (TBI) in adult male Sprague Dawley rats. In the EPITARGET cohort, T2*-weighted MRI was performed at 2, 7, and 21 days and in the EpiBioS4Rx cohort at 2, 9, and 30 days and 5 months post-TBI. Both hippocampi were segmented using convolutional neural networks. The extracted segmentation mask was used for a geometric construction, extracting 39 parameters that described the position and orientation of the left and right hippocampus. In each cohort, we assessed the parameters as prognostic biomarkers for posttraumatic epilepsy (PTE) both individually, using repeated measures analysis of variance, and in combination, using random forest classifiers. Results The extracted parameters were highly effective in discriminating between sham-operated and TBI rats in both the EPITARGET and EpiBioS4Rx cohorts at all timepoints (t; balanced accuracy > .9). The most discriminating parameter was the inclination of the hippocampus ipsilateral to the lesion at t = 2 days and the volumes at t >= 7 days after TBI. Furthermore, in the EpiBioS4Rx cohort, we could effectively discriminate epileptogenic from nonepileptogenic animals with a longer MRI follow-up, at t = 150 days (area under the curve = .78, balanced accuracy = .80, p = .0050), based on the orientation of both hippocampi. We found that the ipsilateral hippocampus rotated outward on the horizontal plane, whereas the contralateral hippocampus rotated away from the vertical direction. Significance We demonstrate that assessment of TBI-induced hippocampal deformation by clinically translatable MRI methodologies detects subjects with prior TBI as well as those at high risk of PTE, paving the way toward subject stratification for antiepileptogenesis studies

PET criteria by cancer type from imaging interpretation to treatment response assessment: beyond FDG PET score

Background: in recent years, the role of positron emission tomography (PET) and PET/computed tomography (PET/CT) has emerged as a reliable diagnostic tool in a wide variety of pathological conditions. This review aims to collect and review PET criteria developed for interpretation and treatment response assessment in cases of non-[18F]fluorodeoxyglucose ([18F]FDG) imaging in oncology. Methods: A wide literature search of the PubMed/MEDLINE, Scopus and Google Scholar databases was made to find relevant published articles about non-[18F]FDG PET response criteria. Results: The comprehensive computer literature search revealed 183 articles. On reviewing the titles and abstracts, 149 articles were excluded because the reported data were not within the field of interest. Finally, 34 articles were selected and retrieved in full-text versions. Conclusions: available criteria are a promising tool for the interpretation of non-FDG PET scans, but also to assess the response to therapy and therefore to predict the prognosis. However, oriented clinical trials are needed to clearly evaluate their impact on patient management

Validation of a New Classification Method of Postoperative Complications in Patients Undergoing Coronary Surgery

International audienceObjective The authors aimed to validate the European Multicenter Study on Coronary Artery Bypass Grafting (E-CABG) classification of postoperative complications in patients undergoing coronary artery bypass grafting (CABG). Design Retrospective, observational study. Setting University hospital. Participants A total of 2,764 patients with severe coronary artery disease. Complete baseline, operative, and postoperative data were available for patients who underwent isolated CABG. Interventions Isolated CABG. Measurements and Main Results The E-CABG complication classification was used to stratify the severity and prognostic impact of adverse postoperative events. Primary outcome endpoints were 30-day, 90-day, and long-term all-cause mortality. The secondary outcome endpoints was the length of intensive care unit stay. Both the E-CABG complication grades and additive score were predictive of 30-day (area under the receiver operating characteristics curve 0.866, 95% confidence interval [CI] 0.829-0.903; and 0.876; 95% CI 0.844-0.908, respectively) and 90-day (area under the receiver operating characteristics curve 0.850, 95% CI 0.812-0.887; and 0.863, 95% CI 0.829-0.897, respectively) all-cause mortality. The complication grades were independent predictors of increased mortality at actuarial (log-rank: p<0.0001) and adjusted analysis (p<0.0001; grade 1: hazard ratio [HR] 1.757, 95% CI 1.111-2.778; grade 2: HR 2.704, 95% CI 1.664-4.394; grade 3: HR 5.081, 95% CI 3.148-8.201). When patients who died within 30 days were excluded from the analysis, this grading method still was associated with late mortality (p<0.0001). The grading method (p<0.0001) and the additive score (rho, 0.514; p<0.0001) were predictive of the length of intensive care unit stay. Conclusions The E-CABG postoperative complication classification seems to be a promising tool for stratifying the severity and prognostic impact of postoperative complications in patients undergoing cardiac surger

Bleeding in Patients Treated With Ticagrelor or Clopidogrel Before Coronary Artery Bypass Grafting

BackgroundWe evaluated perioperative bleeding after coronary artery bypass grafting (CABG) in patients preoperatively treated with ticagrelor or clopidogrel, stratified by discontinuation of these P2Y12 inhibitors.MethodsAll patients from the prospective, European Multicenter Registry on Coronary Artery Bypass Grafting (E-CABG) treated with ticagrelor or clopidogrel undergoing isolated primary CABG were eligible. The primary outcome measure was severe or massive bleeding defined according to the Universal Definition of Perioperative Bleeding, stratified by P2Y12 inhibitor discontinuation. Secondary outcome measures included four additional definitions of major bleeding. Propensity score matching was performed to adjust for differences in preoperative and perioperative covariates.ResultsOf 2,311 patients who were included, 1,293 (55.9%) received clopidogrel and 1,018 (44.1%) ticagrelor preoperatively. Mean time between discontinuation and the operation was 4.5 ± 3.2 days for clopidogrel and 4.9 ± 3.0 days for ticagrelor. In the propensity score–matched cohort, ticagrelor-treated patients had a higher incidence of major bleeding according to Universal Definition of Perioperative Bleeding when ticagrelor was discontinued 0 to 2 days compared with 3 days before the operation (16.0% vs 2.7%, p = 0.003). Clopidogrel-treated patients had a higher incidence of major bleeding according to the Universal Definition of Perioperative Bleeding when clopidogrel was discontinued 0 to 3 days compared with 4 to 5 days before the operation (15.6% vs 8.3%, p = 0.031).ConclusionsIn patients receiving ticagrelor 2 days before CABG and in those receiving clopidogrel 3 days before CABG, there was an increased rate of severe bleeding. Postponing nonemergent CABG for at least 3 days after discontinuation of ticagrelor and 4 days after clopidogrel should be considered.</div