A hierarchical multi-task approach to gastrointestinal image analysis

Comunicació presentada a ICPR International Workshops and Challenges, celebrat del 10 al 15 de gener de 2021 de manera virtual.A large number of different lesions and pathologies can affect the human digestive system, resulting in life-threatening situations. Early detection plays a relevant role in the successful treatment and the increase of current survival rates to, e.g., colorectal cancer. The standard procedure enabling detection, endoscopic video analysis, generates large quantities of visual data that need to be carefully analyzed by an specialist. Due to the wide range of color, shape, and general visual appearance of pathologies, as well as highly varying image quality, such process is greatly dependent on the human operator experience and skill. In this work, we detail our solution to the task of multi-category classification of images from the gastrointestinal (GI) human tract within the 2020 Endotect Challenge. Our approach is based on a Convolutional Neural Network minimizing a hierarchical error function that takes into account not only the finding category, but also its location within the GI tract (lower/upper tract), and the type of finding (pathological finding/therapeutic intervention/anatomical landmark/mucosal views’ quality). We also describe in this paper our solution for the challenge task of polyp segmentation in colonoscopies, which was addressed with a pretrained double encoder-decoder network. Our internal cross-validation results show an average performance of 91.25 Mathews Correlation Coefficient (MCC) and 91.82 Micro-F1 score for the classification task, and a 92.30 F1 score for the polyp segmentation task. The organization provided feedback on the performance in a hidden test set for both tasks, which resulted in 85.61 MCC and 86.96 F1 score for classification, and 91.97 F1 score for polyp segmentation. At the time of writing no public ranking for this challenge had been released

Double encoder-decoder networks for gastrointestinal polyp segmentation

Comunicació presentada a ICPR International Workshops and Challenges, celebrat del 10 al 15 de gener de 2021 de manera virtual.Polyps represent an early sign of the development of Colorectal Cancer. The standard procedure for their detection consists of colonoscopic examination of the gastrointestinal tract. However, the wide range of polyp shapes and visual appearances, as well as the reduced quality of this image modality, turn their automatic identification and segmentation with computational tools into a challenging computer vision task. In this work, we present a new strategy for the delineation of gastrointestinal polyps from endoscopic images based on a direct extension of common encoder-decoder networks for semantic segmentation. In our approach, two pretrained encoder-decoder networks are sequentially stacked: the second network takes as input the concatenation of the original frame and the initial prediction generated by the first network, which acts as an attention mechanism enabling the second network to focus on interesting areas within the image, thereby improving the quality of its predictions. Quantitative evaluation carried out on several polyp segmentation databases shows that double encoder-decoder networks clearly outperform their single encoder-decoder counterparts in all cases. In addition, our best double encoder-decoder combination attains excellent segmentation accuracy and reaches state-of-the-art performance results in all the considered datasets, with a remarkable boost of accuracy on images extracted from datasets not used for training

The Role of indirect mechanotransduction phenomena in microtrauma development within intervertebral discs – a computational biophysical analysis

Comunicació presentada a: IRCOBI Conference 2018, celebrada a Atenes, Grècia, del 12 al 14 de setembre de 2018.The causes of intervertebral disc (IVD) ruptures continue to elude both biomechanists and clinicians. It is widely accepted that unique traumatic high‐load impacts are unlikely to lead to isolated IVD herniation. Therefore, a suggested mechanism for disc rupture is an accumulation of microtrauma within the IVD tissue. Microtrauma development can be related to tissue fatigue caused by repetitive (physiological) load application, as shown, for example, in [1]. However, the impact of long‐term biological influence on microtrauma, such as changes in the tissue quality due to cellular activity, has not yet been extensively addressed. A persistent catabolic shift of cellular activity possibly leads to a reduction of the tissue’s capability to resist loads. Mechanobiological investigations showed that loads acting on the tissue impact cellular predisposition and therefore influence tissue maintenance (e.g. [2]). Loads can be sensed by cells in a direct or an indirect manner. Direct mechanotransduction refers to loads transmitted over the extracellular matrix (ECM) directly on the cell’s membrane, whereas indirect mechanotransduction refers to alteration in the ECM compaction, changes in solute transport to the cells and the effect of this on cell behaviour [3]. We assume that the latter is of special interest with regard to IVD microtrauma emergence because nutrition of Nucleus Pulposus (NP) and inner Annulus Fibrosus (AF) cells is diffusion‐dependent. The objective of this project is to find crucial mechanisms at the tissue and cellular levels that lead to microtrauma within the IVD. This short paper presents the first results to address the influence of indirect mechanotransduction on the predisposition of NP cells to develop catabolic activity and contribute locally to IVD tissue damage

Balanced-mixup for highly imbalanced medical image classification

Comunicació presentada a 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), celebrat del 27 de setembre a l'1 d'octubre de 2021 de manera virtual.Highly imbalanced datasets are ubiquitous in medical image classification problems. In such problems, it is often the case that rare classes associated to less prevalent diseases are severely under-represented in labeled databases, typically resulting in poor performance of machine learning algorithms due to overfitting in the learning process. In this paper, we propose a novel mechanism for sampling training data based on the popular MixUp regularization technique, which we refer to as Balanced-MixUp. In short, Balanced-MixUp simultaneously performs regular (i.e., instance-based) and balanced (i.e., class-based) sampling of the training data. The resulting two sets of samples are then mixed-up to create a more balanced training distribution from which a neural network can effectively learn without incurring in heavily under-fitting the minority classes. We experiment with a highly imbalanced dataset of retinal images (55K samples, 5 classes) and a long-tail dataset of gastro-intestinal video frames (10K images, 23 classes), using two CNNs of varying representation capabilities. Experimental results demonstrate that applying Balanced-MixUp outperforms other conventional sampling schemes and loss functions specifically designed to deal with imbalanced data. Code is released at https://github.com/agaldran/balanced_mixupThis work was partially supported by a Marie Skłodowska-Curie Global Fellowship (No. 892297) and by Australian Research Council grants (DP180103232 and FT190100525)

Volumetric anatomical parameterization and meshing for inter-patient liver coordinate system definition

A coordinate system parameterizing the interior of organs is/na powerful tool for a systematic localization of injured tissue. If the same/ncoordinate values are assigned to speci c anatomical sites, parameterizations/nensure integration of data across di erent medical image modalities./nHarmonic mappings have been used to produce parametric meshes over/nthe surface of anatomical shapes, given their /nexibility to set values at/nspeci c locations through boundary conditions. However, most of the existing/nimplementations in medical imaging restrict to either anatomical/nsurfaces, or the depth coordinate with boundary conditions is given at/nsites of limited geometric diversity. In this paper we present a method for/nanatomical volumetric parameterization that extends current harmonic/nparameterizations to the interior anatomy using information provided by/nthe volume medial surface. We have applied the methodology to de ne/na common reference system for the liver shape and functional anatomy./nThis reference system sets a solid base for creating anatomical models of/nthe patient's liver, and allows comparing livers from several patients in/na common framework of reference.This research has been funded by the Catalan project 2009-TEM-00007, Spanish/nprojects TIN2009-13618, TIN2012-3311, and the European Union FP7 grant agreement/nno. HEAR-EU 30485

The Role of indirect mechanotransduction phenomena in microtrauma development within intervertebral discs – a computational biophysical analysis

Comunicació presentada a: IRCOBI Conference 2018, celebrada a Atenes, Grècia, del 12 al 14 de setembre de 2018.The causes of intervertebral disc (IVD) ruptures continue to elude both biomechanists and clinicians. It is widely accepted that unique traumatic high‐load impacts are unlikely to lead to isolated IVD herniation. Therefore, a suggested mechanism for disc rupture is an accumulation of microtrauma within the IVD tissue. Microtrauma development can be related to tissue fatigue caused by repetitive (physiological) load application, as shown, for example, in [1]. However, the impact of long‐term biological influence on microtrauma, such as changes in the tissue quality due to cellular activity, has not yet been extensively addressed. A persistent catabolic shift of cellular activity possibly leads to a reduction of the tissue’s capability to resist loads. Mechanobiological investigations showed that loads acting on the tissue impact cellular predisposition and therefore influence tissue maintenance (e.g. [2]). Loads can be sensed by cells in a direct or an indirect manner. Direct mechanotransduction refers to loads transmitted over the extracellular matrix (ECM) directly on the cell’s membrane, whereas indirect mechanotransduction refers to alteration in the ECM compaction, changes in solute transport to the cells and the effect of this on cell behaviour [3]. We assume that the latter is of special interest with regard to IVD microtrauma emergence because nutrition of Nucleus Pulposus (NP) and inner Annulus Fibrosus (AF) cells is diffusion‐dependent. The objective of this project is to find crucial mechanisms at the tissue and cellular levels that lead to microtrauma within the IVD. This short paper presents the first results to address the influence of indirect mechanotransduction on the predisposition of NP cells to develop catabolic activity and contribute locally to IVD tissue damage

Volumetric anatomical parameterization and meshing for inter-patient liver coordinate system definition

A coordinate system parameterizing the interior of organs is/na powerful tool for a systematic localization of injured tissue. If the same/ncoordinate values are assigned to speci c anatomical sites, parameterizations/nensure integration of data across di erent medical image modalities./nHarmonic mappings have been used to produce parametric meshes over/nthe surface of anatomical shapes, given their /nexibility to set values at/nspeci c locations through boundary conditions. However, most of the existing/nimplementations in medical imaging restrict to either anatomical/nsurfaces, or the depth coordinate with boundary conditions is given at/nsites of limited geometric diversity. In this paper we present a method for/nanatomical volumetric parameterization that extends current harmonic/nparameterizations to the interior anatomy using information provided by/nthe volume medial surface. We have applied the methodology to de ne/na common reference system for the liver shape and functional anatomy./nThis reference system sets a solid base for creating anatomical models of/nthe patient's liver, and allows comparing livers from several patients in/na common framework of reference.This research has been funded by the Catalan project 2009-TEM-00007, Spanish/nprojects TIN2009-13618, TIN2012-3311, and the European Union FP7 grant agreement/nno. HEAR-EU 30485

Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders

Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind the medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions.This work was partly funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825903 (euCanSHare project)