Mr. Amaan Valiuddin Profile

Amaan Valiuddin

at Technische Univ Eindhoven

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Proceedings Article | 7 April 2023 Presentation + Paper

Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data

Christiaan G. Viviers, M. M. Amaan Valiuddin, Peter H. de With, Fons van der Sommen

Proceedings Volume 12465, 124651I (2023) https://doi.org/10.1117/12.2654255

KEYWORDS: Measurement uncertainty, Deep convolutional neural networks, Spatial data uncertainty, 3D modeling, Education and training, Image segmentation, Computed tomography, Lung, 3D image processing, Voxels, Computer aided detection

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Uncertainty quantification in medical images has become an essential addition to segmentation models for practical application in the real world. Although there are valuable developments in accurate uncertainty quantification methods using 2D images and slices of 3D volumes, in clinical practice, the complete 3D volumes (such as CT and MRI scans) are used to evaluate and plan the medical procedure. As a result, the existing 2D methods miss the rich 3D spatial information when resolving the uncertainty. A popular approach for quantifying the ambiguity in the data is to learn a distribution over the possible hypotheses. In recent work, this ambiguity has been modeled to be strictly Gaussian. Normalizing Flows (NFs) are capable of modelling more complex distributions and thus, better fit the embedding space of the data. To this end, we have developed a 3D probabilistic segmentation framework augmented with NFs, to enable capturing the distributions of various complexity. To test the proposed approach, we evaluate the model on the LIDC-IDRI dataset for lung nodule segmentation and quantify the aleatoric uncertainty introduced by the multi-annotator setting and inherent ambiguity in the CT data. Following this approach, we are the first to present a 3D Squared Generalized Energy Distance (D2 GED) of 0.401 and a high 0.468 Hungarian-matched 3D IoU. The obtained results reveal the value in capturing the 3D uncertainty, using a flexible posterior distribution augmented with a Normalizing Flow. Finally, we present the aleatoric uncertainty in a visual manner with the aim to provide clinicians with additional insight into data ambiguity and facilitating more informed decision-making.

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