A radiomics-based brain network in T1 images: construction, attributes, and applications.

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma.

Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.

Brain Tumor Segmentation for Multi-Modal MRI with Missing Information.

Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. However, developing a network that maintains clinical relevance in situations where certain MRI sequence(s) might be unavailable or unusual poses a significant challenge. While one solution is to train multiple models with different MRI sequence combinations, it is impractical to train every model from all possible sequence combinations. In this paper, we propose a DCNN-based brain tumor segmentation framework incorporating a novel sequence dropout technique in which networks are trained to be robust to missing MRI sequences while employing all other available sequences. Experiments were performed on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. When all MRI sequences were available, there were no significant differences in performance of the model with and without dropout for enhanced tumor (ET), tumor (TC), and whole tumor (WT) (p-values 1.000, 1.000, 0.799, respectively), demonstrating that the addition of dropout improves robustness without hindering overall performance. When key sequences were unavailable, the network with sequence dropout performed significantly better. For example, when tested on only T1, T2, and FLAIR sequences together, DSC for ET, TC, and WT increased from 0.143 to 0.486, 0.431 to 0.680, and 0.854 to 0.901, respectively. Sequence dropout represents a relatively simple yet effective approach for brain tumor segmentation with missing MRI sequences.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study.

OBJECTIVES: In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients. METHODS: A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes: 1021 LVOs and 7629 normal. The Inception-V1 I3D architecture was applied for LVO detection. For outcome prediction, 323 patients undergoing thrombectomy were selected. A 3D convolution neural network (CNN) was used for outcome prediction (30-day mRS) with CTA volumes and embedded pre-treatment variables as inputs. RESULT: For LVO-detection model, CTAs from 8,650 patients (median age 68 years, interquartile range (IQR): 58-81; 3934 females) were analyzed. The cross-validated AUC for LVO vs. not was 0.74 (95% CI: 0.72-0.75). For the mRS classification model, CTAs from 323 patients (median age 75 years, IQR: 63-84; 164 females) were analyzed. The algorithm achieved a test AUC of 0.82 (95% CI: 0.79-0.84), sensitivity of 89%, and specificity 66%. The two models were then integrated with hospital infrastructure where CTA was collected in real-time and processed by the model. If LVO was detected, interventionists were notified and provided with predicted clinical outcome information. CONCLUSION: 3D CNNs based on CTA were effective in selecting LVO and predicting LVO mechanical thrombectomy short-term prognosis. End-to-end AI platform allows users to receive immediate prognosis prediction and facilitates clinical workflow.

Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning.

Reconstructing perceived stimulus (image) only from human brain activity measured with functional Magnetic Resonance Imaging (fMRI) is a significant task in brain decoding. However, the inconsistent distribution and representation between fMRI signals and visual images cause great 'domain gap'. Moreover, the limited fMRI data instances generally suffer from the issues of low signal noise ratio (SNR), extremely high dimensionality, and limited spatial resolution. Existing methods are often affected by these issues so that a satisfactory reconstruction is still an open problem. In this paper, we show that it is possible to obtain a promising solution by learning visually-guided latent cognitive representations from the fMRI signals, and inversely decoding them to the image stimuli. The resulting framework is called Dual-Variational Autoencoder/ Generative Adversarial Network (D-Vae/Gan), which combines the advantages of adversarial representation learning with knowledge distillation. In addition, we introduce a novel three-stage learning strategy which enables the (cognitive) encoder to gradually distill useful knowledge from the paired (visual) encoder during the learning process. Extensive experimental results on both artificial and natural images have demonstrated that our method could achieve surprisingly good results and outperform the available alternatives.

Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR.

BACKGROUND: This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions. METHODS: This retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADCall b, mADC0-1000), BE (mD, mD*, mf), SE (mDDC, mα), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance. RESULTS: RF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC0-1000). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all P < 0.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04). CONCLUSIONS: The radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.

Extensive-dynamic-range and high-resolution surface profiling with phase-sensitive spectral-domain white-light interferometry.

We demonstrate a Fourier-transform-based method for extensive-dynamic-range and high-resolution surface profiling using phase-sensitive spectral-domain white-light interferometry. By combining the frequency and phase of interference fringes, this method is capable of displacement measurement with nanometer-scale resolution and a dynamic range up to several millimeters. The performance of the method is demonstrated by surface profiling of a coin, gauge blocks, and a cell-phone circuit board.

Genome-wide study of C2H2 zinc finger gene family in Medicago truncatula.

BACKGROUND: C2H2 zinc finger proteins (C2H2 ZFPs) play vital roles in shaping many aspects of plant growth and adaptation to the environment. Plant genomes harbor hundreds of C2H2 ZFPs, which compose one of the most important and largest transcription factor families in higher plants. Although the C2H2 ZFP gene family has been reported in several plant species, it has not been described in the model leguminous species Medicago truncatula. RESULTS: In this study, we identified 218 C2H2 type ZFPs with 337 individual C2H2 motifs in M. truncatula. We showed that the high rate of local gene duplication has significantly contributed to the expansion of the C2H2 gene family in M. truncatula. The identified ZFPs exhibit high variation in motif arrangement and expression pattern, suggesting that the short C2H2 zinc finger motif has been adopted as a scaffold by numerous transcription factors with different functions to recognize cis-elements. By analyzing the public expression datasets and quantitative RT-PCR (qRT-PCR), we identified several C2H2 ZFPs that are specifically expressed in certain tissues, such as the nodule, seed, and flower. CONCLUSION: Our genome-wide work revealed an expanded C2H2 ZFP gene family in an important legume M. truncatula, and provides new insights into the diversification and expansion of C2H2 ZFPs in higher plants.

Detection of major loci associated with the variation of 18 important agronomic traits between Solanum pimpinellifolium and cultivated tomatoes.

Wild species can be used to improve various agronomic traits in cultivars; however, a limited understanding of the genetic basis underlying the morphological differences between wild and cultivated species hinders the integration of beneficial traits from wild species. In the present study, we generated and sequenced recombinant inbred lines (RILs, 201 F10 lines) derived from a cross between Solanum pimpinellifolium and Solanum lycopersicum tomatoes. Based on a high-resolution recombination bin map to uncover major loci determining the phenotypic variance between wild and cultivated tomatoes, 104 significantly associated loci were identified for 18 agronomic traits. On average, these loci explained ~39% of the phenotypic variance of the RILs. We further generated near-isogenic lines (NILs) for four identified loci, and the lines exhibited significant differences for the associated traits. We found that two loci could improve the flower number and inflorescence architecture in the cultivar following introgression of the wild-species alleles. These findings allowed us to construct a trait-locus network to help explain the correlations among different traits based on the pleiotropic or linked loci. Our results provide insights into the morphological changes between wild and cultivated tomatoes, and will help to identify key genes governing important agronomic traits for the molecular selection of elite tomato varieties.

Enhancer-Promoter Interaction of SELF PRUNING 5G Shapes Photoperiod Adaptation.

Tomato (Solanum lycopersicum) is a major vegetable fruit grown and consumed worldwide. Modern cultivated tomatoes are derived from their wild relative, Solanum pimpinellifolium, a short-day plant that originated from the Andean region of South America. The molecular underpinnings of the regional adaptation and expansion of domesticated tomato remain largely unclear. In this study, we examined flowering time in wild and cultivated tomatoes under both long-day and short-day conditions. Using quantitative trait locus mapping in a recombinant inbred line population, we identified SELF PRUNING 5G (SP5G) as a major locus influencing daylength adaptation in tomato. Genetic diversity analysis revealed that the genomic region harboring SP5G shows signatures of a domestication sweep. We found that a 52-bp sequence within the 3' untranslated region of SP5G is essential for the enhanced expression of this gene, leading to delayed flowering time in tomatoes through a promoter-enhancer interaction that occurs only under long-day conditions. We further demonstrate that the absence of the 52-bp sequence attenuates the promoter-enhancer interaction and reduces SP5G expression in cultivated tomatoes, making their flowering time insensitive to daylength. Our findings demonstrate that cis-regulatory variation at the enhancer region of the SP5G 3' untranslated region confers reduced photoperiodic response in cultivated tomatoes, uncovering a regulatory mechanism that could potentially be used to manipulate flowering time in tomato through novel biotechnological approaches.

An Implicit-Explicit Prototypical Alignment Framework for Semi-Supervised Medical Image Segmentation.

Semi-supervised learning methods have been explored to mitigate the scarcity of pixel-level annotation in medical image segmentation tasks. Consistency learning, serving as a mainstream method in semi-supervised training, suffers from low efficiency and poor stability due to inaccurate supervision and insufficient feature representation. Prototypical learning is one potential and plausible way to handle this problem due to the nature of feature aggregation in prototype calculation. However, the previous works have not fully studied how to enhance the supervision quality and feature representation using prototypical learning under the semi-supervised condition. To address this issue, we propose an implicit-explicit alignment (IEPAlign) framework to foster semi-supervised consistency training. In specific, we develop an implicit prototype alignment method based on dynamic multiple prototypes on-the-fly. And then, we design a multiple prediction voting strategy for reliable unlabeled mask generation and prototype calculation to improve the supervision quality. Afterward, to boost the intra-class consistency and inter-class separability of pixel-wise features in semi-supervised segmentation, we construct a region-aware hierarchical prototype alignment, which transmits information from labeled to unlabeled and from certain regions to uncertain regions. We evaluate IEPAlign on three medical image segmentation tasks. The extensive experimental results demonstrate that the proposed method outperforms other popular semi-supervised segmentation methods and achieves comparable performance with fully-supervised training methods.

A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer.

This rapid communication highlights the correlations between digital pathology-whole slide imaging (WSI) and radiomics-magnetic resonance imaging (MRI) features in triple-negative breast cancer (TNBC) patients. The research collected 12 patients who had both core needle biopsy and MRI performed to evaluate pathologic complete response (pCR). The results showed that higher collagenous values in pathology data were correlated with more homogeneity, whereas higher tumor expression values in pathology data correlated with less homogeneity in the appearance of tumors on MRI by size zone non-uniformity normalized (SZNN). Higher myxoid values in pathology data are correlated with less similarity of gray-level non-uniformity (GLN) in tumor regions on MRIs, while higher immune values in WSIs correlated with the more joint distribution of smaller-size zones by small area low gray-level emphasis (SALGE) in the tumor regions on MRIs. Pathologic complete response (pCR) was associated with collagen, tumor, and myxoid expression in WSI and GLN and SZNN in radiomic features. The correlations of WSI and radiomic features may further our understanding of the TNBC tumoral microenvironment (TME) and could be used in the future to better tailor the use of neoadjuvant chemotherapy (NAC). This communication will focus on the post-NAC MRI features correlated with pCR and their association with WSI features from core needle biopsies.

A gradient mapping guided explainable deep neural network for extracapsular extension identification in 3D head and neck cancer computed tomography images.

BACKGROUND: Diagnosis and treatment management for head and neck squamous cell carcinoma (HNSCC) is guided by routine diagnostic head and neck computed tomography (CT) scans to identify tumor and lymph node features. The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with HNSCC. It is essential to detect the occurrence of ECE as it changes staging and treatment planning for patients. Current clinical ECE detection relies on visual identification and pathologic confirmation conducted by clinicians. However, manual annotation of the lymph node region is a required data preprocessing step in most of the current machine learning-based ECE diagnosis studies. PURPOSE: In this paper, we propose a Gradient Mapping Guided Explainable Network (GMGENet) framework to perform ECE identification automatically without requiring annotated lymph node region information. METHODS: The gradient-weighted class activation mapping (Grad-CAM) technique is applied to guide the deep learning algorithm to focus on the regions that are highly related to ECE. The proposed framework includes an extractor and a classifier. In a joint training process, informative volumes of interest (VOIs) are extracted by the extractor without labeled lymph node region information, and the classifier learns the pattern to classify the extracted VOIs into ECE positive and negative. RESULTS: In evaluation, the proposed methods are well-trained and tested using cross-validation. GMGENet achieved test accuracy and area under the curve (AUC) of 92.2% and 89.3%, respectively. GMGENetV2 achieved 90.3% accuracy and 91.7% AUC in the test. The results were compared with different existing models and further confirmed and explained by generating ECE probability heatmaps via a Grad-CAM technique. The presence or absence of ECE has been analyzed and correlated with ground truth histopathological findings. CONCLUSIONS: The proposed deep network can learn meaningful patterns to identify ECE without providing lymph node contours. The introduced ECE heatmaps will contribute to the clinical implementations of the proposed model and reveal unknown features to radiologists. The outcome of this study is expected to promote the implementation of explainable artificial intelligence-assiste ECE detection.

Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation.

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes a Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. The visual features of each region are embedded using a novel survival attention mechanism, offering spatially and risk-aware features for sentence encoding while maintaining global coherence across tasks. A cross-domain LLMs-Alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologists. Multi-center experiments validate the overall performance and each module's composition within the model, encouraging further advancements in radiology report generation research emphasizing clinical interpretation and trustworthiness in AI models applied to medical studies.

Association of clinical and imaging characteristics with pulmonary function testing in patients with Long-COVID.

Purpose: The purpose of this study is to identify clinical and imaging characteristics associated with post-COVID pulmonary function decline. Methods: This study included 22 patients recovering from COVID-19 who underwent serial spirometry pulmonary function testing (PFT) before and after diagnosis. Patients were divided into two cohorts by difference between baseline and post-COVID follow-up PFT: Decline group (>10 % decrease in FEV1), and Stable group (≤10 % decrease or improvement in FEV1). Demographic, clinical, and laboratory data were collected, as well as PFT and chest computed tomography (CT) at the time of COVID diagnosis and follow-up. CTs were semi-quantitatively scored on a five-point severity scale for disease extent in each lobe by two radiologists. Mann-Whitney U-tests, T-tests, and Chi-Squared tests were used for comparison. P-values <0.05 were considered statistically significant. Results: The Decline group had a higher proportion of neutrophils (79.47 ± 4.83 % vs. 65.45 ± 10.22 %; p = 0.003), a higher absolute neutrophil count (5.73 ± 2.68 × 109/L vs. 3.43 ± 1.74 × 109/L; p = 0.031), and a lower proportion of lymphocytes (9.90 ± 4.20 % vs. 21.21 ± 10.97 %; p = 0.018) compared to the Stable group. The Decline group also had significantly higher involvement of ground-glass opacities (GGO) on follow-up chest CT [8.50 (4.50, 14.50) vs. 3.0 (1.50, 9.50); p = 0.032] and significantly higher extent of reticulations on chest CT at time of COVID diagnosis [6.50 (4.00, 9.00) vs. 2.00 (0.00, 6.00); p = 0.039] and follow-up [5.00 (3.00, 13.00) vs. 2.00 (0.00, 5.00); p = 0.041]. ICU admission was higher in the Decline group than in the Stable group (71.4 % vs. 13.3 %; p = 0.014). Conclusions: This study provides novel insight into factors influencing post-COVID lung function, irrespective of pre-existing pulmonary conditions. Our findings underscore the significance of neutrophil counts, reduced lymphocyte counts, pulmonary reticulation on chest CT at diagnosis, and extent of GGOs on follow-up chest CT as potential indicators of decreased post-COVID lung function. This knowledge may guide prediction and further understanding of long-term sequelae of COVID-19 infection.

De-Biased Disentanglement Learning for Pulmonary Embolism Survival Prediction on Multimodal Data.

Health disparities among marginalized populations with lower socioeconomic status significantly impact the fairness and effectiveness of healthcare delivery. The increasing integration of artificial intelligence (AI) into healthcare presents an opportunity to address these inequalities, provided that AI models are free from bias. This paper aims to address the bias challenges by population disparities within healthcare systems, existing in the presentation of and development of algorithms, leading to inequitable medical implementation for conditions such as pulmonary embolism (PE) prognosis. In this study, we explore the diverse bias in healthcare systems, which highlights the demand for a holistic framework to reducing bias by complementary aggregation. By leveraging de-biasing deep survival prediction models, we propose a framework that disentangles identifiable information from images, text reports, and clinical variables to mitigate potential biases within multimodal datasets. Our study offers several advantages over traditional clinical-based survival prediction methods, including richer survival-related characteristics and bias-complementary predicted results. By improving the robustness of survival analysis through this framework, we aim to benefit patients, clinicians, and researchers by enhancing fairness and accuracy in healthcare AI systems.

Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.

Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies. Uncertainty estimation techniques including Bayesian estimation with dropout, bootstrapping, and margins sampling were compared to random query. Strategies to avoid annotating similar images were also considered. We determined the minimum data necessary to achieve performance equivalent to the model trained on the full dataset (α = 0.05). Bayesian approximation with dropout at training and testing showed results equivalent to that of the full data model (target) with around 30% of the training data needed by random query to achieve target performance (p = 0.018). Annotation redundancy restriction techniques can reduce the training data needed by random query to achieve target performance by 20%. We investigated various active learning strategies to minimize the annotation burden for three-dimensional brain tumor segmentation. Dropout uncertainty estimation achieved target performance with the least annotated data.