# Automated CT Lung Cancer Screening Workflow using 3D Camera

arXiv: https://arxiv.org/abs/2309.15750 · HTML: https://mlsanity.com/paper/2309.15750/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [OSSO: Obtaining Skeletal Shape from Outside](https://mlsanity.com/paper/2204.10129/) (2022)
- [H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes](https://mlsanity.com/paper/1709.07330/) (2017)
- [Generating Synthetic X-ray Images of a Person from the Surface Geometry](https://mlsanity.com/paper/1805.00553/) (2018)
- [Adaloss: Adaptive Loss Function for Landmark Localization](https://mlsanity.com/paper/1908.01070/) (2019)

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

- [Internal Organ Localization Using Depth Images](https://mlsanity.com/paper/2503.23468/) (2025)
- [Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow](https://mlsanity.com/paper/2601.18260/) (2026)

## Abstract (arXiv)

Despite recent developments in CT planning that enabled automation in patient positioning, time-consuming scout scans are still needed to compute dose profile and ensure the patient is properly positioned. In this paper, we present a novel method which eliminates the need for scout scans in CT lung cancer screening by estimating patient scan range, isocenter, and Water Equivalent Diameter (WED) from 3D camera images. We achieve this task by training an implicit generative model on over 60,000 CT scans and introduce a novel approach for updating the prediction using real-time scan data. We demonstrate the effectiveness of our method on a testing set of 110 pairs of depth data and CT scan, resulting in an average error of 5mm in estimating the isocenter, 13mm in determining the scan range, 10mm and 16mm in estimating the AP and lateral WED respectively. The relative WED error of our method is 4%, which is well within the International Electrotechnical Commission (IEC) acceptance criteria of 10%.