An open-source framework for pulmonary fissure completeness assessment.
James C Ross, Pietro Nardelli, Jorge Onieva, Sarah E Gerard, Rola Harmouche, Yuka Okajima, Alejandro A Diaz, George Washko, Raúl San José Estépar
Author Information
James C Ross: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States. Electronic address: jross@bwh.harvard.edu.
Pietro Nardelli: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Jorge Onieva: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Biomedical Image Technologies Laboratory (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain.
Sarah E Gerard: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Rola Harmouche: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Yuka Okajima: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Alejandro A Diaz: Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
George Washko: Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Raúl San José Estépar: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
We present an open-source framework for pulmonary fissure completeness assessment. Fissure incompleteness has been shown to associate with emphysema treatment outcomes, motivating the development of tools that facilitate completeness estimation. Generally, the task of fissure completeness assessment requires accurate detection of fissures and definition of the boundary surfaces separating the lung lobes. The framework we describe acknowledges a) the modular nature of fissure detection and lung lobe segmentation (lobe boundary detection), and b) that methods to address these challenges are varied and continually developing. It is designed to be readily deployable on existing lung lobe segmentation and fissure detection data sets. The framework consists of multiple components: a flexible quality control module that enables rapid assessment of lung lobe segmentations, an interactive lobe segmentation tool exposed through 3D Slicer for handling challenging cases, a flexible fissure representation using particles-based sampling that can handle fissure feature-strength or binary fissure detection images, and a module that performs fissure completeness estimation using voxel counting and a novel surface area estimation approach. We demonstrate the usage of the proposed framework by deploying on 100 cases exhibiting various levels of fissure completeness. We compare the two completeness level approaches and also compare to visual reads. The code is available to the community via github as part of the Chest Imaging Platform and a 3D Slicer extension module.
