18 research outputs found
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Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies
This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Hus{\o}y were used to compare traditional segmentation techniques (co-occurrence, filtering, local binary patterns, watershed, multiresolution sub-band filtering) against a deep-learning approach based on the U-Net architecture. For the latter, the effects of depth of the network, number of epochs and different optimisation algorithms were investigated. Overall, the best results were provided by the deep-learning approach. However, the best results were distributed within the parameters, and many configurations provided results well below the traditional techniques
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Volumetric analysis of HeLa cancer cells imaged with serial block face scanning electron microscopy
This dissertation investigates the volumetric analysis of a variety of cervical cancer cells called HeLa cells. HeLa cells were derived from cervical cancer cells taken from Henrietta Lacks at the Johns Hopkins Hospital and hence the name HeLa remains. The shape of cells is important as the regular or irregular shape of the cell and its structures can be related to some conditions of health or disease.
In this dissertation, a traditional image processing algorithm to segment the nuclear envelope of HeLa cells imaged with Serial Block Face Scanning Electron Microscopy is proposed. The algorithm is fast, robust and accurate and it was compared against different deep learning architectures. Three deep learning architectures were deployed through transfer learning and U-Net was trained from scratch for semantic segmentation of HeLa cells. The algorithm outperformed all four deep learning architectures and active contours (snakes) in both accuracy and time as suggested by the similarity metrics. The segmented nuclear envelope was further investigated through a visualisation technique to obtain a graphical model. This model provides volume and surface metrics which can be used to compare different cells. Wild-type of HeLa cells were compared against Chlamydia trachomatis-infected HeLa cells and geometric differences were revealed.
The open-source image processing algorithm, developed in programming environment of Matlab® (The MathworksTM, Natick, USA), provides cell segmentation in a fraction of manual segmentation time therefore it is an alternative to expensive commercial software and manual segmentation, which is still widely used despite the significant disadvantages of time and inter- and intra-user variability
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Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images
This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches
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Morphological estimation of Cellularity on Neo-adjuvant treated breast cancer histological images
This paper describes a methodology that extracts morphological features from histological breast cancer images stained for Hematoxilyn and Eosin (H&E). Cellularity was estimated and the correlation between features and the residual tumour size cellularity after a Neo-Adjuvant treatment (NAT) was examined. Images from whole slide imaging (WSI) were processed automatically with traditional computer vision methods to extract twenty two morphological parameters from the nuclei, epithelial region and the global image. The methodology was applied to a set of images from breast cancer under NAT. The data came from the BreastPathQ Cancer Cellularity Challenge 2019, and consisted of 2579 patches of 255×255 pixels of H&E histopatological samples from NAT treatment patients. The methodology automatically implements colour separation, segmentation and morphological analysis using traditional algorithms (K-means grouping, watershed segmentation, Otsu’s binarisation). Linear regression methods were applied to determine strongest correlation between the parameters and the cancer cellularity. The morphological parameters showed correlation with the residual tumour cancer cellularity. The strongest correlations corresponded to the stroma concentration value (r = −0.9786) and value from HSV image colour space (r = −0.9728), both from a global image parameters
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Estimation of cellularity in tumours treated with Neoadjuvant therapy: A comparison of Machine Learning algorithms
This paper describes a method for residual tumour cellularity (TC) estimation in Neoadjuvant treatment (NAT) of advanced breast cancer. This is determined manually by visual inspection by a radiologist, then an automated computation will contribute to reduce time workload and increase precision and accuracy. TC is estimated as the ratio of tumour area by total image area estimated after the NAT. The method proposed computes TC by using machine learning techniques trained with information on morphological parameters of segmented nuclei in order to classify regions of the image as tumour or normal. The data is provided by the 2019 SPIE Breast challenge, which was proposed to develop automated TC computation algorithms. Three algorithms were implemented: Support Vector Machines, Nearest K-means and Adaptive Boosting (AdaBoost) decision trees. Performance based on accuracy is compared and evaluated and the best result was obtained with Support Vector Machines. Results obtained by the methods implemented were submitted during ongoing challenge with a maximum of 0.76 of prediction probability of success
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Segmentation and modelling of hela nuclear envelope
This paper describes an algorithm to segment the 3D nuclear envelope of HeLa cancer cells from electron microscopy images and model the volumetric shape of the nuclear envelope against an ellipsoid. The algorithm was trained on a single cell and then tested in six separate cells. To assess the algorithm, Jaccard similarity index and Hausdorff distance against a manually-delineated gold standard were calculated on two cells. The mean Jaccard value and Hausdorff distance that the segmentation achieved for central slices were 98% and 4 pixels for the first cell and 94% and 13 pixels for the second cell and outperformed segmentation with active contours. The modelling projects a 3D to a 2D surface that summarises the complexity of the shape in an intuitive result. Measurements extracted from the modelled surface may be useful to correlate shape with biological characteristics. The algorithm is unsupervised, fully automatic, fast and processes one image in less than 10 seconds. Code and data are freely available at https://github.com/reyesaldasoro/Hela-Cell-Segmentation and http://dx.doi.org/10.6019/EMPIAR-10094
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Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures
The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%), and U-Net (92%, 56%)
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Geometric differences between nuclear envelopes of Wild-type and Chlamydia trachomatis-infected HeLa cells
In this work, the geometrical characteristics of two different types of cells observed with Electron Microscopy were analysed. The nuclear envelope of Wild-type HeLa cells and Chlamydia trachomatis-infected HeLa cells were automatically segmented and then modelled against a spheroid and converted to a two-dimensional surface. Geometric measurements from this surface and the volumetric nuclear envelope were extracted to compare the two types of cells. The measurements included the nuclear volume, the sphericity of the nucleus, its flatness or spikiness. In total 13 different cells were segmented: 7 Wild-type and 6 Chlamydia trachomatis-infected. The cells were statistically different in the following measurements. Wild-type HeLa cells have greater volumes than that of Chlamydia trachomatis-infected HeLa cells and they are more spherical as Jaccard index suggests. Standard deviation (σ), and range of values for the nuclear envelope, which shows the distance of the highest peaks and deepest valleys from the spheroid, were also extracted from the modelling against a spheroid and these metrics were used to compare two different data sets in order to draw conclusions
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Radiography Classification: A comparison between Eleven Convolutional Neural Networks
This paper investigates the classification of normal and abnormal radiographic images. Eleven convolutional neural network architectures (GoogleNet, Vgg-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, Vgg-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2) were used to classify a series of x-ray images from Stanford Musculoskeletal Radiographs (MURA) dataset corresponding to the wrist images of the data base. For each architecture, the results were compared against the known labels (normal / abnormal) and then the following metrics were calculated: accuracy (labels correctly classified) and Cohen's kappa (a measure of agreement) following MURA guidelines. Numerous experiments were conducted by changing classifiers (Adam, Sgdm, RmsProp), the number of epochs, with/without data augmentation. The best results were provided by InceptionResnet-v2 (Mean accuracy = 0.723, Mean Kappa = 0.506). Interestingly, these results lower than those reported in the Leaderboard of MURA. We speculate that to improve the results from basic CNN architectures several options could be tested, for instance: pre-processing, post-processing or domain knowledge, and ensembles
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Relationship Between Irregularities of the Nuclear Envelope and Mitochondria in HeLa cells Observed with Electron Microscopy
This paper describes a methodology to analyse the complexity of HeLa cells as observed with electron microscopy, in particular the relationship between mitochondria and the roughness of the nuclear envelope as reflected by the invaginations of the surface. For this purpose, several segmentation mitochondria algorithms were quantitatively compared, namely: Topology, Image Processing, Topology and Image Processing, and Deep Learning, which provided the highest accuracy. The invaginations were successfully segmented with one image processing algorithm. Metrics were extracted for both structures and correlations between the mitochondria and invaginations were explored for 25 segmented cells. It was found that there was a positive correlation between the volume of invaginations and the volume of mitochondria, and negative correlations between the number and the mean volume of mitochondria, and between the volume of the cytoplasm and the aspect ratio of mitochondria. These results suggest that there is a relationship between the shape of a cell, its nucleus and its mitochondria; as well as a relationship between the number of mitochondria and their shapes. Whilst these results were obtained from a single cell line and a relatively small number of cells, they encourage further study as the methodology proposed can be easily applied to other cells and settings