Lung Nodules Detection and Segmentation Using 3D Mask-RCNN

TL;DR

This paper adapts the 2D Mask-RCNN architecture for 3D lung nodule detection and segmentation in CT scans, improving workflow and patient care by automating a traditionally manual process.

Contribution

It introduces a novel 3D extension of Mask-RCNN for simultaneous detection and segmentation of lung nodules from CT scans, achieving competitive results.

Findings

Competitive detection results on LUNA16 dataset

Framework provides both detection and 3D segmentation

Enhances workflow efficiency in radiology

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 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.