1. Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets
- Author
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Chlap, P, Min, H, Dowling, J, Field, M, Cloak, K, Leong, T, Lee, M, Chu, J, Tan, J, Tran, P, Kron, T, Sidhom, M, Wiltshire, K, Keats, S, Kneebone, A, Haworth, A, Ebert, MA, Vinod, SK, Holloway, L, Chlap, P, Min, H, Dowling, J, Field, M, Cloak, K, Leong, T, Lee, M, Chu, J, Tan, J, Tran, P, Kron, T, Sidhom, M, Wiltshire, K, Keats, S, Kneebone, A, Haworth, A, Ebert, MA, Vinod, SK, and Holloway, L
- Abstract
BACKGROUND AND OBJECTIVES: Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not. METHODS: We use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by mul
- Published
- 2024