27 research outputs found
Final infarct prediction in acute ischemic stroke
This article focuses on the control center of each human body: the brain. We
will point out the pivotal role of the cerebral vasculature and how its complex
mechanisms may vary between subjects. We then emphasize a specific acute
pathological state, i.e., acute ischemic stroke, and show how medical imaging
and its analysis can be used to define the treatment. We show how the
core-penumbra concept is used in practice using mismatch criteria and how
machine learning can be used to make predictions of the final infarct, either
via deconvolution or convolutional neural networks.Comment: 17 pages, 5 figures, part of PhD thesis KU Leuven 2022 "Understanding
Final Infarct Prediction in Acute Ischemic Stroke Using Convolutional Neural
Networks
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
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
The soft Dice loss (SDL) has taken a pivotal role in many automated
segmentation pipelines in the medical imaging community. Over the last years,
some reasons behind its superior functioning have been uncovered and further
optimizations have been explored. However, there is currently no implementation
that supports its direct use in settings with soft labels. Hence, a synergy
between the use of SDL and research leveraging the use of soft labels, also in
the context of model calibration, is still missing. In this work, we introduce
Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a
standard setting with hard labels, but (ii) can be used in settings with soft
labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm
the potential synergy of DMLs with soft labels (e.g. averaging, label
smoothing, and knowledge distillation) over hard labels (e.g. majority voting
and random selection). As a result, we obtain superior Dice scores and model
calibration, which supports the wider adoption of DMLs in practice. Code is
available at
\href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: Submitted to MICCAI2023. Code is available at
https://github.com/zifuwanggg/JDTLosse
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 methodâs 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 Unionâs Horizon 2020 Research and Innovations Programme and an innovation mandate of Flanders Innovation and Entrepreneurship (VLAIO).http://www.elsevier.com/locate/mediahj2022Anatom
Towards fully automated third molar development staging in panoramic radiographs
Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the staging process by employing the full potential of deep learning, using convolutional neural networks (CNNs) in every step of the procedure. The dataset used to train the CNNs consisted of 400 panoramic radiographs (OPGs), with 20 OPGs per developmental stage per sex, staged in consensus between three observers. The concepts of transfer learning, using pre-trained CNNs, and data augmentation were used to mitigate the issues when dealing with a limited dataset. In this work, a three-step procedure was proposed and the results were validated using fivefold cross-validation. First, a CNN localized the geometrical center of the lower left third molar, around which a square region of interest (ROI) was extracted. Second, another CNN segmented the third molar within the ROI. Third, a final CNN used both the ROI and the segmentation to classify the third molar into its developmental stage. The geometrical center of the third molar was found with an average Euclidean distance of 63 pixels. Third molars were segmented with an average Dice score of 93%. Finally, the developmental stages were classified with an accuracy of 54%, a mean absolute error of 0.69 stages, and a linear weighted Cohenâs kappa coefficient of 0.79. The entire automated workflow on average took 2.72 s to compute, which is substantially faster than manual staging starting from the OPG. Taking into account the limited dataset size, this pilot study shows that the proposed fully automated approach shows promising results compared with manual staging.Internal Funds KU Leuvenhttp://link.springer.com/journal/4142021-04-01hj2020Anatom
DeepVoxNet: voxelâwise prediction for 3D images
Several important medical image analysis tasks can be cast as a voxel-wise prediction: e.g. segmentation of structures of interest or regression of a new modality from others. Convolutional neural networks (CNNs) are the state-of-the-art approach for voxel-wise prediction and high-quality software such as Keras is available to define, train and use them. To harness them in the context of medical image analysis problems, additional components are necessary to handle the memory requirements of 3D processing, the various image modalities that are typically combined and the appropriate data augmentation. We have created DeepVoxNet to provide these components, and let researchers and developers quickly use CNNs on medical image analysis problems in an efficient and flexible fashion.status: Published onlin
Effect of lower third molar segmentations on automated tooth development staging using a convolutional neural network
Staging third molar development is commonly used for age estimation in subadults. Automated developmental stage allocation to the mandibular left third molar in panoramic radiographs has been examined in a pilot study. This method used an AlexNet Deep Convolutional Neural Network (CNN) approach to stage lower left third molars, which had been selected by manually drawn bounding boxes around them. This method (bounding box AlexNet = BA) still contained parts of surrounding structures which may have affected the automated stage allocation performance. We hypothesize that segmenting only the third molar could further improve the automated stage allocation performance. Therefore, the current study aimed to determine and validate the effect of lower third molar segmentations on automated tooth development staging. Retrospectively, 400 panoramic radiographs were collected, processed and segmented in three ways: bounding box (BB), rough (RS), and full (FS) tooth segmentation. A DenseNet201 CNN was used for automated stage allocation. Automated staging results were compared with reference stages - allocated by human observers - overall and per stage. FS rendered the best results with a stage allocation accuracy of 0.61, a mean absolute difference of 0.53 stages and a Cohen's linear kappa of 0.84. Misallocated stages were mostly neighboring stages, and DenseNet201 rendered better results than AlexNet by increasing the percentage of correctly allocated stages by 3% (BA compared to BB). FS increased the percentage of correctly allocated stages by 7% compared to BB. In conclusion, full tooth segmentation and a DenseNet CNN optimize automated dental stage allocation for age estimation