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Fig. 1 | BMC Medical Imaging

Fig. 1

From: Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks

Fig. 1

Flow-chart showing data in- and exclusion together with segmentation for network training. From a total number of 90 contrast-enhanced CT scans contained in the publicly available dataset 1 CT scan was excluded because it did not contain the complete scan of the thorax. Further, a total of 690 LNs were excluded because of an SAD < 5 mm. CT scans containing mediastinal bulky disease and bulky axillary lymphadenopathy were not excluded. Top left image—exemplary segmentation of bulky axillary lymphadenopathy; top right image—exemplary segmentation of normal axillary LNs; Bottom left image—exemplary segmentation of bulky mediastinal lymphadenopathy; Bottom right image—exemplary segmentation of enlarged LNs. Considerable differences in image quality of the different CT scans was noted as exemplarily shown in the bottom right image. CT computed tomography, LNs lymph nodes, SAD short-axis diameter

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