  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 CT
scan or on segmenting them from a small 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.
