Xuan V. Nguyen

Engin Dikici, Xuan V. Nguyen, Matthew Bigelow et al.

The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. We previously developed a framework for detecting small BM (with diameters of less than 15mm) in T1-weighted Contrast-Enhanced 3D Magnetic Resonance images (T1c) to assist medical experts in this time-sensitive and high-stakes task. The framework utilizes a dedicated convolutional neural network (CNN) trained using labeled T1c data, where the ground truth BM segmentations were provided by a radiologist. This study aims to advance the framework with a noisy student-based self-training strategy to make use of a large corpus of unlabeled T1c data (i.e., data without BM segmentations or detections). Accordingly, the work (1) describes the student and teacher CNN architectures, (2) presents data and model noising mechanisms, and (3) introduces a novel pseudo-labeling strategy factoring in the learned BM detection sensitivity of the framework. Finally, it describes a semi-supervised learning strategy utilizing these components. We performed the validation using 217 labeled and 1247 unlabeled T1c exams via 2-fold cross-validation. The framework utilizing only the labeled exams produced 9.23 false positives for 90% BM detection sensitivity; whereas, the framework using the introduced learning strategy led to ~9% reduction in false detections (i.e., 8.44) for the same sensitivity level. Furthermore, while experiments utilizing 75% and 50% of the labeled datasets resulted in algorithm performance degradation (12.19 and 13.89 false positives respectively), the impact was less pronounced with the noisy student-based training strategy (10.79 and 12.37 false positives respectively).

Ujjwal Baid, Satyam Ghodasara, Suyash Mohan et al.

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor's molecular characterization, in pre-operative baseline mpMRI data from 2,040 patients. Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The performance evaluation of all participating algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top ranked participants monetary awards of $60,000 collectively.

Sema Candemir, Xuan V. Nguyen, Luciano M. Prevedello et al.

Purpose: This study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients by processing only the clinical and imaging data collected at the initial visit. Approach: We built a predictive model based on a supervised hybrid neural network utilizing a 3-Dimensional Convolutional Neural Network to perform volume analysis of Magnetic Resonance Imaging and integration of non-imaging clinical data at the fully connected layer of the architecture. The experiments are conducted on the Alzheimers Disease Neuroimaging Initiative dataset. Results: Experimental results confirm that there is a correlation between cognitive decline and the data obtained at the first visit. The system achieved an area under the receiver operator curve (AUC) of 0.70 for cognitive decline class prediction. Conclusion: To our knowledge, this is the first study that predicts slowly deteriorating/stable or rapidly deteriorating classes by processing routinely collected baseline clinical and demographic data (Baseline MRI, Baseline MMSE, Scalar Volumetric data, Age, Gender, Education, Ethnicity, and Race). The training data is built based on MMSE-rate values. Unlike the studies in the literature that focus on predicting Mild Cognitive Impairment-to-Alzheimer`s disease conversion and disease classification, we approach the problem as an early prediction of cognitive decline rate in MCI patients.