# Lung Nodules Detection and Segmentation Using 3D Mask-RCNN

arXiv: https://arxiv.org/abs/1907.07676 · HTML: https://mlsanity.com/paper/1907.07676/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling](https://mlsanity.com/paper/2301.13319/) (2023)
- [EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy](https://mlsanity.com/paper/2402.14099/) (2024)

## Abstract (arXiv)

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.