Lung Nodules Detection and Segmentation Using 3D Mask-RCNN

Evi Kopelowitz, Guy Englehard

Keywords: Lung Nodule detection, CNN, 3D segmentation, Deep learning, Mask-RCNN

Abstract: Accurate assessment of Lung nodules is a time consuming and error prone ingredient of the radiologist interpretation work. Automating 3D volume detection and segmentation can improve workflow as well as patient care. Previous works have focused either on detecting lung nodules from a full Computed Tomography (CT) scan or on segmenting them from a small Region Of Interest (ROI). We adapt the state of the art architecture for 2D object detection and segmentation, MaskRCNN, to handle 3D images and employ it to detect and segment lung nodules from CT scans. We report on competitive results for the lung nodule detection on LUNA16 data set. The added value of our method is that in addition to lung nodule detection, our framework produces 3D segmentations of the detected nodules.

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