Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture

Karabağ, Cefa; Verhoeven, Jo; Miller, Naomi R.; Reyes-Aldasoro, Constantino Carlos

doi:10.3390/app9183900

Cited by 23 publications

(15 citation statements)

References 63 publications

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“…The U-Net can be trained end-to-end from relatively few pairs or patches of images and their corresponding classes. Applications of U-Nets include cell counting, detection, and morphometry, [87], automatic brain tumour detection and segmentation [88] and texture segmentation [89].…”

Section: Plos Onementioning

confidence: 99%

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

et al. 2020

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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:

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Section: Plos Onementioning

confidence: 99%

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

et al. 2020

Self Cite

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“…In this architecture the encoder and decoder ideally represent the rails of the ladder and the rungs their connections. The U-Net has symmetric contracting and expanding paths, and concatenates the feature maps from the encoder to the corresponding upsampled maps from the decoder by copy and crop (69,70). This allows the decoder to reconstruct relevant features that are lost when pooled in the encoder.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT

Bianconi¹,

Fravolini²,

Pizzoli³

et al. 2021

Quant Imaging Med Surg

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Background: Accurate segmentation of pulmonary nodules on computed tomography (CT) scans plays a crucial role in the evaluation and management of patients with suspicion of lung cancer (LC). When performed manually, not only the process requires highly skilled operators, but is also tiresome and timeconsuming. To assist the physician in this task several automated and semi-automated methods have been proposed in the literature. In recent years, in particular, the appearance of deep learning has brought about major advances in the field.Methods: Twenty-four (12 conventional and 12 based on deep learning) semi-automated-'one-click'methods for segmenting pulmonary nodules on CT were evaluated in this study. The experiments were carried out on two datasets: a proprietary one (383 images from a cohort of 111 patients) and a public one (259 images from a cohort of 100). All the patients had a positive transcript for suspect pulmonary nodules. Results:The methods based on deep learning clearly outperformed the conventional ones. The best performance [Sørensen-Dice coefficient (DSC)] in the two datasets was, respectively, 0.853 and 0.763 for the deep learning methods, and 0.761 and 0.704 for the traditional ones.Conclusions: Deep learning is a viable approach for semi-automated segmentation of pulmonary nodules on CT scans.

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“…Assuming that kw and kw are the width and height of the convolution kernel, respectively, 𝑊 ′ and 𝐻 ′ are calculated as 𝑊 ′ =W-kw+1 and 𝐻 ′ =H-kh+1. Each filter in the output feature map was obtained by adding the individual filter weights of the three input channels (R, G, and B) [37,38]. In this study, the features were extracted using 2D separable convolution, where the features were extracted from each channel separately.…”

Section: Phase Ii: Feature Extractionmentioning

confidence: 99%

Feature Extraction of White Blood Cells Using CMYK-Moment Localization and Deep Learning in Acute Myeloid Leukemia Blood Smear Microscopic Images

et al. 2022

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Artificial intelligence has revolutionized medical diagnosis, particularly for cancers. Acute myeloid leukemia (AML) diagnosis is a tedious protocol that is prone to human and machine errors. In several instances, it is difficult to make an accurate final decision even after careful examination by an experienced pathologist. However, computer-aided diagnosis (CAD) can help reduce the errors and time associated with AML diagnosis. White Blood Cells (WBC) detection is a critical step in AML diagnosis, and deep learning is considered a state-of-the-art approach for WBC detection. However, the accuracy of WBC detection is strongly associated with the quality of the extracted features used in training the pixel-wise classification models. CAD depends on studying the different patterns of changes associated with WBC counts and features. In this study, a new hybrid feature extraction method was developed using image processing and deep learning methods. The proposed method consists of two steps: 1) a region of interest (ROI) is extracted using the CMYK-moment localization method and 2) deep learning-based features are extracted using a CNN-based feature fusion method. Several classification algorithms are used to evaluate the significance of the extracted features. The proposed feature extraction method was evaluated using an external dataset and benchmarked against other feature extraction methods. The proposed method achieved excellent performance, generalization, and stability using all the classifiers, with overall classification accuracies of 97.57% and 96.41% using the primary and secondary datasets, respectively. This method has opened a new alternative to improve the detection of WBCs, which could lead to a better diagnosis of AML.INDEX TERMS Acute myeloid leukemia (AML), white blood cell (WBC) feature extraction, deep learning, feature fusion, CNN, ROI.

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Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture

Cited by 23 publications

References 63 publications

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT

Feature Extraction of White Blood Cells Using CMYK-Moment Localization and Deep Learning in Acute Myeloid Leukemia Blood Smear Microscopic Images

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