Robust Segmentation Models using an Uncertainty Slice Sampling-Based Annotation Workflow

AbstractIn this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components, and then ensembled them to obtain robust segmentation maps. This reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from BRATS 2019 challenge were used for training the models. Further, we tested a classical machine learning algorithm (xgboost) with features extracted from the segmentation maps to classify subject survival range. Preliminary results on the BRATS 2019 validation dataset demonstrasted this method can achieve excellent performance with DICE scores of 0.898, 0.784, 0.779 for whole tumor, tumor core and enhancing tumor respectively and accuracy 34.5 % for survuval prediction.

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Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models

BMC Bioinformatics ◽

10.1186/s12859-021-04221-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Arnaud Liehrmann ◽

Guillem Rigaill ◽

Toby Dylan Hocking

Keyword(s):

Histone Modifications ◽

Count Data ◽

High Throughput Sequencing ◽

Genetic Regulation ◽

Regulation Of Gene Expression ◽

Basic Mechanism ◽

R Package ◽

Detection Accuracy ◽

Full Potential ◽

Segmentation Models

Abstract Background Histone modification constitutes a basic mechanism for the genetic regulation of gene expression. In early 2000s, a powerful technique has emerged that couples chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq). This technique provides a direct survey of the DNA regions associated to these modifications. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed or adapted to analyze the massive amount of data it generates. Many of these algorithms were built around natural assumptions such as the Poisson distribution to model the noise in the count data. In this work we start from these natural assumptions and show that it is possible to improve upon them. Results Our comparisons on seven reference datasets of histone modifications (H3K36me3 & H3K4me3) suggest that natural assumptions are not always realistic under application conditions. We show that the unconstrained multiple changepoint detection model with alternative noise assumptions and supervised learning of the penalty parameter reduces the over-dispersion exhibited by count data. These models, implemented in the R package CROCS (https://github.com/aLiehrmann/CROCS), detect the peaks more accurately than algorithms which rely on natural assumptions. Conclusion The segmentation models we propose can benefit researchers in the field of epigenetics by providing new high-quality peak prediction tracks for H3K36me3 and H3K4me3 histone modifications.

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Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

BMC Medical Imaging ◽

10.1186/s12880-021-00551-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Markus J. Ankenbrand ◽

Liliia Shainberg ◽

Michael Hock ◽

David Lohr ◽

Laura M. Schreiber

Keyword(s):

Neural Networks ◽

Sensitivity Analysis ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Cardiac Mri ◽

Magnetic Resonance Images ◽

Model Interpretation ◽

First Case ◽

Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation

Applied Sciences ◽

10.3390/app11157046 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7046

Author(s):

Jorge Francisco Ciprián-Sánchez ◽

Gilberto Ochoa-Ruiz ◽

Lucile Rossi ◽

Frédéric Morandini

Keyword(s):

Deep Learning ◽

Loss Function ◽

State Of The Art ◽

Fire Detection ◽

Loss Functions ◽

Wildfire Spread ◽

Combine Information ◽

The Impact ◽

Image Type ◽

Segmentation Models

Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models.

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A Robust Segmentation Algorithmfor for 3D Complex Meshes

Journal of Physics Conference Series ◽

10.1088/1742-6596/1802/3/032045 ◽

2021 ◽

Vol 1802 (3) ◽

pp. 032045

Author(s):

Yu Hou ◽

Yong Zhao

Keyword(s):

Robust Segmentation

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EyeHealer: A large-scale anterior eye segment dataset with eye structure and lesion annotations

Precision Clinical Medicine ◽

10.1093/pcmedi/pbab009 ◽

2021 ◽

Author(s):

Wenjia Cai ◽

Jie Xu ◽

Ke Wang ◽

Xiaohong Liu ◽

Wenqin Xu ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Image Annotation ◽

Clinical Care ◽

Significant Proportion ◽

Anterior Segment ◽

Future Research ◽

Learning Models ◽

Anterior Eye Segment ◽

Segmentation Models

Abstract Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation) and lens diseases. The construction of an automatic tool for the segmentation of anterior segment eye lesions will greatly improve the efficiency of clinical care. With research on artificial intelligence progressing in recent years, deep learning models have shown their superiority in image classification and segmentation. The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise, however, such data are relatively scarce in the domain of medicine. Herein, the authors developed a new medical image annotation system, called EyeHealer. It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level. Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation. The results showed that semantic segmentation models outperformed medical segmentation models. This paper describes the establishment of the system for automated classification and segmentation tasks. The dataset will be made publicly available to encourage future research in this area.

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Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model

Journal of Personalized Medicine ◽

10.3390/jpm11050364 ◽

2021 ◽

Vol 11 (5) ◽

pp. 364

Author(s):

Bingjiang Qiu ◽

Hylke van der van der Wel ◽

Joep Kraeima ◽

Haye Hendrik Glas ◽

Jiapan Guo ◽

...

Keyword(s):

State Of The Art ◽

Maxillofacial Surgery ◽

Cone Beam ◽

Oral And Maxillofacial Surgery ◽

Metal Artifacts ◽

Shape Model ◽

High Attenuation ◽

Mandible Shape ◽

Prior Shape ◽

Segmentation Models

Accurate mandible segmentation is significant in the field of maxillofacial surgery to guide clinical diagnosis and treatment and develop appropriate surgical plans. In particular, cone-beam computed tomography (CBCT) images with metal parts, such as those used in oral and maxillofacial surgery (OMFS), often have susceptibilities when metal artifacts are present such as weak and blurred boundaries caused by a high-attenuation material and a low radiation dose in image acquisition. To overcome this problem, this paper proposes a novel deep learning-based approach (SASeg) for automated mandible segmentation that perceives overall mandible anatomical knowledge. SASeg utilizes a prior shape feature extractor (PSFE) module based on a mean mandible shape, and recurrent connections maintain the continuity structure of the mandible. The effectiveness of the proposed network is substantiated on a dental CBCT dataset from orthodontic treatment containing 59 patients. The experiments show that the proposed SASeg can be easily used to improve the prediction accuracy in a dental CBCT dataset corrupted by metal artifacts. In addition, the experimental results on the PDDCA dataset demonstrate that, compared with the state-of-the-art mandible segmentation models, our proposed SASeg can achieve better segmentation performance.

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