Exploring synthetic datasets for computer-aided detection: a case study using phantom scan data for enhanced lung nodule false positive reduction.

Purpose: Synthetic datasets hold the potential to offer cost-effective alternatives to clinical data, ensuring privacy protections and potentially addressing biases in clinical data. We present a method leveraging such datasets to train a machine learning algorithm applied as part of a computer-aided detection (CADe) system.

Approach: Our proposed approach utilizes clinically acquired computed tomography (CT) scans of a physical anthropomorphic phantom into which manufactured lesions were inserted to train a machine learning algorithm.

Segmentation and tracking of lung nodules via graph-cuts incorporating shape prior and motion from 4D CT.

Purpose: We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four-dimensional (4D) CT. The method could prove useful in IMRT of lung cancer.

Methods: We modified the conventional graph-cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data.

Incorporating shape prior into active contours with a sparse linear combination of training shapes: application to corpus callosum segmentation.

In this paper, a novel method of embedding shape information into level set image segmentation is proposed. Our method is based on inferring shape variations by a sparse linear combination of instances in the shape repository. Given a sufficient number of training shapes with variations, a new shape can be approximated by a linear span of training shapes associated with those variations.

4D lung tumor segmentation via shape prior and motion cues.

Lung tumor segmentation is important for therapy in the radiation treatment of patients with thoracic malignancies. In this paper, we describe a 4D image segmentation method based on graph-cuts optimization, shape prior and optical flow. Due to small size, the location, and low contrast between the tumor and the surrounding tissue, tumor segmentation in 3D+t is challenging.