Semantic segmentation of the micro-structure of strain-hardening cement-based composites (SHCC) by applying deep learning on micro-computed tomography scans

Abstract Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1,006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the Cancer Imaging Archive (TCIA) pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.

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Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion

Journal of Medical Imaging ◽

10.1117/1.jmi.7.1.012705 ◽

2020 ◽

Vol 7 (01) ◽

pp. 1

Author(s):

Eyjólfur Gudmundsson ◽

Christopher M. Straus ◽

Feng Li ◽

Samuel G. Armato

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Pleural Effusion ◽

Malignant Pleural Mesothelioma ◽

Pleural Mesothelioma ◽

Computed Tomography Scans

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Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

Journal of Medical Internet Research ◽

10.2196/26151 ◽

2021 ◽

Vol 23 (7) ◽

pp. e26151

Author(s):

Stanislav Nikolov ◽

Sam Blackwell ◽

Alexei Zverovitch ◽

Ruheena Mendes ◽

Michelle Livne ◽

...

Keyword(s):

Computed Tomography ◽

At Risk ◽

Deep Learning ◽

Clinical Practice ◽

Head And Neck ◽

Organs At Risk ◽

Data Set ◽

Computed Tomography Scans ◽

Neck Anatomy ◽

Organ At Risk

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

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A redescription of the Late Jurassic (Tithonian) turtle Uluops uluops and a new phylogenetic hypothesis of Paracryptodira

Swiss Journal of Palaeontology ◽

10.1186/s13358-021-00234-y ◽

2021 ◽

Vol 140 (1) ◽

Author(s):

Yann Rollot ◽

Serjoscha W. Evers ◽

Walter G. Joyce

Keyword(s):

Computed Tomography ◽

Phylogenetic Analysis ◽

Late Jurassic ◽

Internal Carotid ◽

Circulation System ◽

Phylogenetic Hypothesis ◽

Micro Computed Tomography ◽

Future Studies ◽

Computed Tomography Scans ◽

Palatine Artery

AbstractWe study the Late Jurassic (Tithonian) turtle Uluops uluops using micro-computed tomography scans to investigate the cranial anatomy of paracryptodires, and provide new insights into the evolution of the internal carotid artery and facial nerve systems, as well as the phylogenetic relationships of this group. We demonstrate the presence of a canalis caroticus lateralis in Uluops uluops, the only pleurosternid for which a palatine artery canal can be confidently identified. Our phylogenetic analysis retrieves Uluops uluops as the earliest branching pleurosternid, Helochelydridae within Pleurosternidae, and Compsemydidae including Kallokibotion bajazidi within Baenidae, which suggests at least two independent losses of the palatine artery within paracryptodires. We expect future studies will provide additional insights into the evolution of the circulation system of paracryptodires, as well as clarifying relationships along the turtle stem.

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Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Frontiers in Medical Technology ◽

10.3389/fmedt.2021.767836 ◽

2021 ◽

Vol 3 ◽

Author(s):

Dan Luo ◽

Wei Zeng ◽

Jinlong Chen ◽

Wei Tang

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Automatic Segmentation ◽

Medical Image Analysis ◽

Semantic Segmentation ◽

Data Sources ◽

X Rays ◽

Advantages And Disadvantages ◽

Backbone Network ◽

Active Research

Deep learning has become an active research topic in the field of medical image analysis. In particular, for the automatic segmentation of stomatological images, great advances have been made in segmentation performance. In this paper, we systematically reviewed the recent literature on segmentation methods for stomatological images based on deep learning, and their clinical applications. We categorized them into different tasks and analyze their advantages and disadvantages. The main categories that we explored were the data sources, backbone network, and task formulation. We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images. For the backbone network, we distinguished methods based on convolutional neural networks from those based on transformers. We divided task formulations into semantic segmentation tasks and instance segmentation tasks. Toward the end of the paper, we discussed the challenges and provide several directions for further research on the automatic segmentation of stomatological images.

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Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage

BRAIN. BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE ◽

10.18662/brain/12.4/236 ◽

2021 ◽

Vol 12 (4) ◽

pp. 01-27

Author(s):

Sezin Barin ◽

Murat Saribaş ◽

Beyza Gülizar Çiltaş ◽

Gür Emre Güraksin ◽

Utku Köse

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Early Diagnosis ◽

Intracranial Hemorrhage ◽

Hybrid Method ◽

Computer Aided Design ◽

Cranial Computed Tomography ◽

Critical Problem ◽

Computed Tomography Scans ◽

Aided Design

Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.

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