Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty

The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method's clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.Comment: 18 pages, 7 figures, 3 tables, published in Elsevier Medical Image Analysis (2021

Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty

The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method’s clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.NEXIS (www.nexis-project.eu), a project that has received funding from the European Union’s Horizon 2020 Research and Innovations Programme and an innovation mandate of Flanders Innovation and Entrepreneurship (VLAIO).http://www.elsevier.com/locate/mediahj2022Anatom

An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization

The human external ears, or pinnae, have an intriguing shape and, like most parts of the human external body, bilateral symmetry is observed between left and right. It is a well-known part of our auditory sensory system and mediates the spatial localization of incoming sounds in 3D from monaural cues due to its shape-specific filtering as well as binaural cues due to the paired bilateral locations of the left and right ears. Another less broadly appreciated aspect of the human pinna shape is its uniqueness from one individual to another, which is on the level of what is seen in fingerprints and facial features. This makes pinnae very useful in human identification, which is of great interest in biometrics and forensics. Anatomically, the type of symmetry observed is known as matching symmetry, with structures present as separate mirror copies on both sides of the body, and in this work we report the first such investigation of the human pinna in 3D. Within the framework of geometric morphometrics, we started by partitioning ear shape, represented in a spatially dense way, into patterns of symmetry and asymmetry, following a two-factor anova design. Matching symmetry was measured in all substructures of the pinna anatomy. However, substructures that stick out' such as the helix, tragus, and lobule also contained a fair degree of asymmetry. In contrast, substructures such as the conchae, antitragus, and antihelix expressed relatively stronger degrees of symmetric variation in relation to their levels of asymmetry. Insights gained from this study were injected into an accompanying identification setup exploiting matching symmetry where improved performance is demonstrated. Finally, possible implications of the results in the context of ear recognition as well as sound localization are discussed

Statistically Deformable Face Models for Cranio-Facial Reconstruction

Forensic facial reconstruction aims at estimating the facial outlook associated to an unknown skull specimen. Estimation is based on tabulated average values of soft tissue thicknesses measured at a sparse set of landmarks on the skull. Traditional \u27plastic\u27 methods apply modeling clay or plasticine on a cast of the skull approximating the estimated tissue depths at the landmarks and interpolating in between. Current computerized techniques mimic this landmark interpolation procedure using a single facial surface template. However, the resulting reconstruction is biased by the specific choice of the template and no face specific regularization is present. We reduce the bias by using a flexible statistical model of a dense set of facial surface points combined with an associated sparse set of skull landmarks. The statistical model also provides a probability value, which can be used to regularize the reconstruction towards more plausible outlooks. The reconstruction is obtained by fitting the model skull landmarks to the corresponding landmarks indicated on a digital copy of the skull to be reconstructed. The fitting process alternates between changing the face-specific statistical model parameters and interpolating the remaining landmark fit error using a minimal bending Thin-Plate Spline (TPS) based deformation. Furthermore, estimated properties of the skull specimen (BMI, age and gender e.g.) can be incorporated as conditions on the reconstruction by removing property-related shape variation from the statistical model description before the fitting process. This iterative statistical model based reconstruction process is shown by experiment to converge to a realistic reconstruction of the face, independent of the initial template

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi

Simultaneous Segmentation and Anatomical Labeling of the Cerebral Vasculature

We present a novel algorithm for the simultaneous segmentation and anatomical labeling of the cerebral vasculature. The method first constructs an over-complete graph capturing the vasculature. It then selects and labels the subset of edges that most likely represents the true vasculature. Unlike existing approaches that first attempt to obtain a good segmentation and then perform labeling, we jointly optimize for both by simultaneously taking into account the image evidence and the prior knowledge about the geometry and connectivity of the vasculature. This results in an Integer Program (IP), which we solve optimally using a branch-and-cut algorithm. We evaluate our approach on a public dataset of 50 cerebral MRA images, and demonstrate that it compares favorably against state-of-the-art methods