# Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy

arXiv: https://arxiv.org/abs/2202.03978 · HTML: https://mlsanity.com/paper/2202.03978/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [Universality of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1805.10769/) (2018)
- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [Universal approximations of invariant maps by neural networks](https://mlsanity.com/paper/1804.10306/) (2018)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network](https://mlsanity.com/paper/1902.05020/) (2019)
- [One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking](https://mlsanity.com/paper/1907.04641/) (2019)
- [FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registration](https://mlsanity.com/paper/1811.09243/) (2018)
- [Zero Shot Learning for Multi-Modal Real Time Image Registration](https://mlsanity.com/paper/1908.06213/) (2019)

## Built on by (most cited first, 3 of 3)

- [Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy](https://mlsanity.com/paper/2210.05673/) (2022)
- [Region Specific Optimization (RSO)-based Deep Interactive Registration](https://mlsanity.com/paper/2203.04295/) (2022)
- [Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy](https://mlsanity.com/paper/2206.03413/) (2022)

## Abstract (arXiv)

Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images. Propagating expert-drawn contours from the pre-treatment planning CT (pCT) through traditional or deep learning (DL) based deformable image registration (DIR) can achieve improved results in many situations. Typical DL-based DIR models are population based, that is, trained with a dataset for a population of patients, so they may be affected by the generalizability problem. In this paper, we propose a method called test-time optimization (TTO) to refine a pre-trained DL-based DIR population model, first for each individual test patient, and then progressively for each fraction of online ART treatment. Our proposed method is less susceptible to the generalizability problem, and thus can improve overall performance of different DL-based DIR models by improving model accuracy, especially for outliers. Our experiments used data from 239 patients with head and neck squamous cell carcinoma to test the proposed method. Firstly, we trained a population model with 200 patients, and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models. We compared each of the individualized models with the population model in terms of segmentation accuracy. The number of patients with at least 0.05 DSC improvement or 2 mm HD95 improvement by TTO averaged over the 17 selected structures for the state-of-the-art architecture Voxelmorph is 10 out of 39 test patients. The average time for deriving the individualized model using TTO from the pre-trained population model is approximately four minutes. When adapting the individualized model to a later fraction of the same patient, the average time is reduced to about one minute and the accuracy is slightly improved.