Sentence examples for nodule segmentation from inspiring English sources

To overcome these drawbacks, we propose here an algorithm for graphite nodule segmentation based on the Level Set technique.

Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis.

In this paper, an approach is proposed for pulmonary nodule segmentation and feature extraction using multilevel thresholding.

However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation.

Unlike traditional studies primarily relying on cautious nodule segmentation and time-consuming feature extraction, we tackle a more challenging task on directly modeling raw nodule patches and building an end-to-end machine-learning architecture for classifying lung nodule malignancy suspiciousness.

In this series of patients we spent few minutes from thyroid nodule segmentation (about 3 min/patient including also time to retrieve and download images) to features extraction (about 2 min/patient depending on ROI volume).

Segmentations were considered as inadequate if performed at least two slices away from the slice most often selected by all the readers or not only on the pre-identified nodule; these segmentations were excluded from the analysis.

Even though some commercially available software provides specific volumetric algorithms for non-solid nodules, the segmentation is often poor and manual correction is often required.

If this visual validation of the nodule showed incorrect segmentation, the reader tried to segment the nodule three times with the same algorithm before concluding that the nodule could not be correctly segmented by this algorithm.

In 154 of the 545 nodules (28%), one (10%) or both (18%) readers were unable to correctly segment the nodule using all available segmentation algorithms.

First, individual tumour nodules were identified by segmentation on the basis of selection of pixels with intensity values above a threshold that corresponded to background fluorescence from the culture media containing BPD-MA.