Annotation Tools

Pulmonary Nodule Annotation Tool

Lung nodule annotation is especially useful for the development, training, and evaluation of computer-assisted diagnostic methods for lung cancer detection and diagnosis. Manual nodule annotations can be time-consuming. This tool is designed to assist radiologists to mark and record the thoracic nodule findings in 3D chest CT images.

This annotation tool can ease the manual annotation process by providing essential annotation interactions to assist radiologists in quickly marking, measuring, and managing the nodule annotations consistently.
  • Load DICOM CT images for annotation with basic image viewing functions, such as adjusting brightness, contrast, and magnification for easier interpretation of image data
  • Create a nodule annotation by clicking the center of a nodule on an axial view
  • Measure and record the longest axis and shortest axis in axial view using the ruler annotator
  • Choose the texture type of each annotated nodule, including solid, mixed, or GGO
  • Provides a panel of nodules for easier navigation among different nodules
  • Remove and modify an existing nodule annotation
  • Outputs nodule annotations for each CT image into a CSV file

NLP-based Semantic Analysis

This semantic analysis tool is based on NLP techniques to automatically parse out the dependencies of words from free text. Users can then manually make further changes to make corrections if there are any. The parsed results will be used to convert unstructured, narrative clinical notes, or reports into structured data for efficient data management.

This deep learning-based NLP tool uses automated word segmentation, part-of-speech tagging, and dependency parsing to semantically and syntactically analyze medical report texts.

  • Automatic word segmentation
  • Dependency parsing