Tissue classification plays a crucial role towards the understanding of normal neural development, brain-behavior relationships, and the disease mechanisms of a number of psychiatric and neurological illnesses. Ensuring the quality of tissue classification is important for the translation of imaging biomarkers to clinical practice. Because the accuracy of segmentation must be assessed with the human eye, quality assurance becomes more difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, which is an open-source web application for the collaborative quality control of neuroimaging processing outputs. Mindcontrol consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented; they are the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volume and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy to implement quality control protocol for large and small studies alike.