scholarly journals A deep learning-based approach for localization of pedicle regions in preoperative CT scans

10.29007/j56f ◽  
2018 ◽  
Author(s):  
Hooman Esfandiari ◽  
Carolyn Anglin ◽  
Pierre Guy ◽  
Antony J. Hodgson
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Preoperative Planning ◽  
Initial Guess ◽  
Neurological Injury ◽  
Ct Scans ◽  
3D Registration ◽  
Similarity Coefficients ◽  
Implant Position ◽  
Preoperative Ct

Pedicle screw fixation is a common yet technically demanding procedure. Due to the proximity of the inserted implant to the spinal column, a malplaced screw can cause neurological injury and subsequent postoperative complications. A common surgical routine starts with preoperative volumetric image acquisition (e.g. computed tomography) based on which the surgeons can highlight the planned trajectory. This process is generally done manually , which is error prone and time consuming.The primary purpose of this paper is to develop an automatic pedicle region localization based on preoperative CTs. This system can automatically annotate the CT scans to identify the regions corresponding to the pedicles and thus provide important information about the anatomical placement of the CT scan that can be useful for intraoperative implant position assessment (e.g. to initialize the 2D-3D registration). On the other hand, the pedicle localization can be exploited for preoperative planning.We designed and evaluated a fully convolutional neural network for the task of pedicle localization. A large training, validation and testing datasets (5000, 1000, 1000 images separately) were created using a custom data augmentation process that could generate unique vertebral morphologies for each image. After evaluation on the validation and test data, the Dice similarity coefficients between the pedicle regions detected by the trained network and the ground truth was 0.85 and 0.83 respectively.The proposed deep-learning-based algorithm was capable of automatically localizing the regions corresponding to the pedicles based on the preoperative CT scans. Therefore, a reliable initial guess for the 2D-3D registration process needed for intraoperative implant position assessment can be achieved. This system also has potential use in automating the preoperative planning.

scholarly journals COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans

2021 ◽  
Vol 4 ◽  
Author(s):  
Shahin Heidarian ◽  
Parnian Afshar ◽  
Nastaran Enshaei ◽  
Farnoosh Naderkhani ◽  
Moezedin Javad Rafiee ◽  
...  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Data Augmentation ◽  
Spatial Information ◽  
Area Under The Curve ◽  
Spatial Relations ◽  
Building Blocks ◽  
Ct Scans ◽  
Rapid Separation

The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate medical equipment and resources more efficiently. The early diagnosis of this disease will lead to the rapid separation of COVID-19 and non-COVID cases, which will be helpful for health care authorities to optimize resource allocation plans and early prevention of the disease. In this regard, a growing number of studies are investigating the capability of deep learning for early diagnosis of COVID-19. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation and large datasets to identify detailed spatial relations between image instances. Furthermore, existing algorithms utilizing CT scans, either extend slice-level predictions to patient-level ones using a simple thresholding mechanism or rely on a sophisticated infection segmentation to identify the disease. In this paper, we propose a two-stage fully automated CT-based framework for identification of COVID-19 positive cases referred to as the “COVID-FACT”. COVID-FACT utilizes Capsule Networks, as its main building blocks and is, therefore, capable of capturing spatial information. In particular, to make the proposed COVID-FACT independent from sophisticated segmentations of the area of infection, slices demonstrating infection are detected at the first stage and the second stage is responsible for classifying patients into COVID and non-COVID cases. COVID-FACT detects slices with infection, and identifies positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19, community acquired pneumonia, and normal cases. Based on our experiments, COVID-FACT achieves an accuracy of 90.82%, a sensitivity of 94.55%, a specificity of 86.04%, and an Area Under the Curve (AUC) of 0.98, while depending on far less supervision and annotation, in comparison to its counterparts.

Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT

2021 ◽  
Author(s):  
Aditi Iyer ◽  
Maria Thor ◽  
Ifeanyirochukwu Onochie ◽  
Jennifer Hesse ◽  
Kaveh Zakeri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Treatment Planning ◽  
Contextual Information ◽  
Ct Scans ◽  
Institutional Research ◽  
Similarity Coefficients ◽  
Inter Observer Variability ◽  
Segmentation Accuracy ◽  
Deep Learning Model ◽  
Segmentation Models

Abstract ObjectiveDelineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process.ApproachCT scans of 242 head and neck (H&N) cancer patients acquired from 2004-2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded architecture was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021.Main resultsMedians and inter-quartile ranges of Dice Similarity Coefficients (DSC) computed on the retrospective testing set (N=24) were 0.87 (0.85-0.89) for the masseters, 0.80 (0.79- 0.81) for the medial pterygoids, 0.81 (0.79-0.84) for the larynx, and 0.69 (0.67-0.71) for the constrictor. Auto-segmentations, when compared to inter-observer variability in 10 randomly selected scans, showed better agreement (DSC) with each observer as compared to inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request via https://github.com/cerr/CERR/wiki/Auto-Segmentation-models.SignificanceWe developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Additionally, the segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.

scholarly journals Using deep learning (artificial intelligence) to determine the malignant potential of pancreatic cystic neoplasms with preoperative ct scans

HPB ◽  
2019 ◽  
Vol 21 ◽  
pp. S22
Author(s):  
W.B. Lyman ◽  
M. Passeri ◽  
K. Murphy ◽  
D.A. Iannitti ◽  
J.B. Martinie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Malignant Potential ◽  
Ct Scans ◽  
Cystic Neoplasms ◽  
Preoperative Ct ◽  
Pancreatic Cystic Neoplasms

scholarly journals Deep learning-based auto-segmentation of swallowing and chewing structures

2019 ◽  
Author(s):  
Aditi Iyer ◽  
Maria Thor ◽  
Rabia Haq ◽  
Joseph O. Deasy ◽  
Aditya P. Apte
Keyword(s):  
Deep Learning ◽  
Contextual Information ◽  
Observer Agreement ◽  
Ct Scans ◽  
Similarity Coefficients ◽  
Constrictor Muscle ◽  
Tissue Contrast ◽  
Left And Right ◽  
The Individual ◽  
Segmentation Models

AbstractPurposeDelineating the swallowing and chewing structures in Head and Neck (H&N) CT scans is necessary for radiotherapy treatment (RT) planning to reduce the incidence of radiation-induced dysphagia, trismus, and speech dysfunction. Automating this process would decrease the manual input required and yield reproducible segmentations, but generating accurate segmentations is challenging due to the complex morphology of swallowing and chewing structures and limited soft tissue contrast in CT images.MethodsWe trained deep learning models using 194 H&N CT scans from our institution to segment the masseters (left and right), medial pterygoids (left and right), larynx, and pharyngeal constrictor muscle using DeepLabV3+ with the resnet-101 backbone. Models were trained in a sequential manner to guide the localization of each structure group based on prior segmentations. Additionally, an ensemble of models was developed using contextual information from three different views (axial, coronal, and sagittal), for robustness to occasional failures of the individual models. Output probability maps were averaged, and voxels were assigned labels corresponding to the class with the highest combined probability.ResultsThe median dice similarity coefficients (DSC) computed on a hold-out set of 24 CT scans were 0.87±0.02 for the masseters, 0.80±0.03 for the medial pterygoids, 0.81±0.04 for the larynx, and 0.69±0.07for the constrictor muscle. The corresponding 95th percentile Hausdorff distances were 0.32±0.08cm (masseters), 0.42±0.2cm (medial pterygoids), 0.53±0.3cm (larynx), and 0.36±0.15cm (constrictor muscle). Dose-volume histogram (DVH) metrics previously found to correlate with each toxicity were extracted from manual and auto-generated contours and compared between the two sets of contours to assess clinical utility. Differences in DVH metrics were not found to be statistically significant (p>0.05) for any of the structures. Further, inter-observer variability in contouring was studied in 10 CT scans. Automated segmentations were found to agree better with each of the observers as compared to inter-observer agreement, measured in terms of DSC.ConclusionsWe developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT. The resulting segmentations can be included in treatment planning to limit complications following RT for H&N cancer. The segmentation models developed in this work are distributed for research use through the open-source platform CERR, accessible at https://github.com/cerr/CERR.

scholarly journals Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans

2020 ◽  
Author(s):  
Xin He ◽  
Shihao Wang ◽  
Shaohuai Shi ◽  
Xiaowen Chu ◽  
Jiangping Tang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Health Care Professionals ◽  
Data Augmentation ◽  
Model Performance ◽  
Chest Ct ◽  
Ct Scans ◽  
Data Sets ◽  
Learning Models ◽  
3D Cnn ◽  
Novel Coronavirus

AbstractCOVID-19 pandemic has spread all over the world for months. As its transmissibility and high pathogenicity seriously threaten people’s lives, the accurate and fast detection of the COVID-19 infection is crucial. Although many recent studies have shown that deep learning based solutions can help detect COVID-19 based on chest CT scans, there lacks a consistent and systematic comparison and evaluation on these techniques. In this paper, we first build a clean and segmented CT dataset called Clean-CC-CCII by fixing the errors and removing some noises in a large CT scan dataset CC-CCII with three classes: novel coronavirus pneumonia (NCP), common pneumonia (CP), and normal controls (Normal). After cleaning, our dataset consists of a total of 340,190 slices of 3,993 scans from 2,698 patients. Then we benchmark and compare the performance of a series of state-of-the-art (SOTA) 3D and 2D convolutional neural networks (CNNs). The results show that 3D CNNs outperform 2D CNNs in general. With extensive effort of hyperparameter tuning, we find that the 3D CNN model DenseNet3D121 achieves the highest accuracy of 88.63% (F1-score is 88.14% and AUC is 0.940), and another 3D CNN model ResNet3D34 achieves the best AUC of 0.959 (accuracy is 87.83% and F1-score is 86.04%). We further demonstrate that the mixup data augmentation technique can largely improve the model performance. At last, we design an automated deep learning methodology to generate a lightweight deep learning model MNas3DNet41 that achieves an accuracy of 87.14%, F1-score of 87.25%, and AUC of 0.957, which are on par with the best models made by AI experts. The automated deep learning design is a promising methodology that can help health-care professionals develop effective deep learning models using their private data sets. Our Clean-CC-CCII dataset and source code are available at:https://github.com/arthursdays/HKBU HPML COVID-19.

Deep Learning in the Detection of Rare Fractures – Development of a “Deep Learning Convolutional Network” Model for Detecting Acetabular Fractures

2021 ◽  
Author(s):  
Felix Erne ◽  
Daniel Dehncke ◽  
Steven C. Herath ◽  
Fabian Springer ◽  
Nico Pfeifer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pilot Study ◽  
Data Augmentation ◽  
Training Data ◽  
Ct Scans ◽  
Pelvic Injury ◽  
Acetabular Fractures ◽  
Fracture Detection ◽  
Deep Convolutional Neural Networks ◽  
Detection Rates

Abstract Background Fracture detection by artificial intelligence and especially Deep Convolutional Neural Networks (DCNN) is a topic of growing interest in current orthopaedic and radiological research. As learning a DCNN usually needs a large amount of training data, mostly frequent fractures as well as conventional X-ray are used. Therefore, less common fractures like acetabular fractures (AF) are underrepresented in the literature. The aim of this pilot study was to establish a DCNN for detection of AF using computer tomography (CT) scans. Methods Patients with an acetabular fracture were identified from the monocentric consecutive pelvic injury registry at the BG Trauma Center XXX from 01/2003 – 12/2019. All patients with unilateral AF and CT scans available in DICOM-format were included for further processing. All datasets were automatically anonymised and digitally post-processed. Extraction of the relevant region of interests was performed and the technique of data augmentation (DA) was implemented to artificially increase the number of training samples. A DCNN based on Med3D was used for autonomous fracture detection, using global average pooling (GAP) to reduce overfitting. Results From a total of 2,340 patients with a pelvic fracture, 654 patients suffered from an AF. After screening and post-processing of the datasets, a total of 159 datasets were enrolled for training of the algorithm. A random assignment into training datasets (80%) and test datasets (20%) was performed. The technique of bone area extraction, DA and GAP increased the accuracy of fracture detection from 58.8% (native DCNN) up to an accuracy of 82.8% despite the low number of datasets. Conclusion The accuracy of fracture detection of our trained DCNN is comparable to published values despite the low number of training datasets. The techniques of bone extraction, DA and GAP are useful for increasing the detection rates of rare fractures by a DCNN. Based on the used DCNN in combination with the described techniques from this pilot study, the possibility of an automatic fracture classification of AF is under investigation in a multicentre study.

scholarly journals Cross-Domain Data Augmentation for Deep-Learning-Based Male Pelvic Organ Segmentation in Cone Beam CT

2020 ◽  
Vol 10 (3) ◽  
pp. 1154 ◽  
Author(s):  
Jean Léger ◽  
Eliott Brion ◽  
Paul Desbordes ◽  
Christophe De Vleeschouwer ◽  
John A. Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Cone Beam Ct ◽  
Ct Scans ◽  
Cone Beam ◽  
Dice Similarity Coefficient ◽  
Clinical Workflow ◽  
Training Set ◽  
Cross Domain ◽  
Planning Ct

For prostate cancer patients, large organ deformations occurring between radiotherapy treatment sessions create uncertainty about the doses delivered to the tumor and surrounding healthy organs. Segmenting those regions on cone beam CT (CBCT) scans acquired on treatment day would reduce such uncertainties. In this work, a 3D U-net deep-learning architecture was trained to segment bladder, rectum, and prostate on CBCT scans. Due to the scarcity of contoured CBCT scans, the training set was augmented with CT scans already contoured in the current clinical workflow. Our network was then tested on 63 CBCT scans. The Dice similarity coefficient (DSC) increased significantly with the number of CBCT and CT scans in the training set, reaching 0.874 ± 0.096 , 0.814 ± 0.055 , and 0.758 ± 0.101 for bladder, rectum, and prostate, respectively. This was about 10% better than conventional approaches based on deformable image registration between planning CT and treatment CBCT scans, except for prostate. Interestingly, adding 74 CT scans to the CBCT training set allowed maintaining high DSCs, while halving the number of CBCT scans. Hence, our work showed that although CBCT scans included artifacts, cross-domain augmentation of the training set was effective and could rely on large datasets available for planning CT scans.

Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

2020 ◽  
Vol 64 (4) ◽  
pp. 40412-1-40412-11
Author(s):  
Kexin Bai ◽  
Qiang Li ◽  
Ching-Hsin Wang
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
A Priori ◽  
Class Imbalance ◽  
Maximum Flow ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Similarity Coefficients ◽  
Segmentation Algorithms ◽  
Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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