# Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning

arXiv: https://arxiv.org/abs/1912.13335 · HTML: https://mlsanity.com/paper/1912.13335/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [Dual-Stage Deeply Supervised Attention-based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans](https://mlsanity.com/paper/2210.03739/) (2022)
- [MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan](https://mlsanity.com/paper/2304.01576/) (2023)
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- [Explainable multiple abnormality classification of chest CT volumes](https://mlsanity.com/paper/2111.12215/) (2021)
- [3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis](https://mlsanity.com/paper/2207.13830/) (2022)
- [A 2D Sinogram-Based Approach to Defect Localization in Computed Tomography](https://mlsanity.com/paper/2401.16104/) (2024)
- [Complex Swin Transformer for Accelerating Enhanced SMWI Reconstruction](https://mlsanity.com/paper/2512.22202/) (2025)
- [MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection](https://mlsanity.com/paper/2211.00003/) (2022)
- [Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging](https://mlsanity.com/paper/2411.10100/) (2024)
- [Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI](https://mlsanity.com/paper/2512.20436/) (2025)

## Abstract (arXiv)

Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, which can enhance patient survival possibilities. A number of nodule segmentation techniques have been proposed, however, all of the existing techniques rely on radiologist 3-D volume of interest (VOI) input or use the constant region of interest (ROI) and only investigate the presence of nodule voxels within the given VOI. Such approaches restrain the solutions to investigate the nodule presence outside the given VOI and also include the redundant structures into VOI, which may lead to inaccurate nodule segmentation. In this work, a novel semi-automated approach for 3-D segmentation of nodule in volumetric computerized tomography (CT) lung scans has been proposed. The proposed technique can be segregated into two stages, at the first stage, it takes a 2-D ROI containing the nodule as input and it performs patch-wise investigation along the axial axis with a novel adaptive ROI strategy. The adaptive ROI algorithm enables the solution to dynamically select the ROI for the surrounding slices to investigate the presence of nodule using deep residual U-Net architecture. The first stage provides the initial estimation of nodule which is further utilized to extract the VOI. At the second stage, the extracted VOI is further investigated along the coronal and sagittal axis with two different networks and finally, all the estimated masks are fed into the consensus module to produce the final volumetric segmentation of nodule. The proposed approach has been rigorously evaluated on the LIDC dataset, which is the largest publicly available dataset. The result suggests that the approach is significantly robust and accurate as compared to the previous state of the art techniques.