scholarly journals Automatic vertebrae localization and segmentation in CT with a two-stage Dense-U-Net

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pengfei Cheng ◽  
Yusheng Yang ◽  
Huiqiang Yu ◽  
Yongyi He
Keyword(s):  
Detection Rate ◽  
Region Of Interest ◽  
Computer Assisted Surgery ◽  
Computer Assisted ◽  
Location Error ◽  
Dice Coefficient ◽  
Two Stage ◽  
Second Stage ◽  
Vertebrae Segmentation ◽  
Whole Spine

AbstractAutomatic vertebrae localization and segmentation in computed tomography (CT) are fundamental for spinal image analysis and spine surgery with computer-assisted surgery systems. But they remain challenging due to high variation in spinal anatomy among patients. In this paper, we proposed a deep-learning approach for automatic CT vertebrae localization and segmentation with a two-stage Dense-U-Net. The first stage used a 2D-Dense-U-Net to localize vertebrae by detecting the vertebrae centroids with dense labels and 2D slices. The second stage segmented the specific vertebra within a region-of-interest identified based on the centroid using 3D-Dense-U-Net. Finally, each segmented vertebra was merged into a complete spine and resampled to original resolution. We evaluated our method on the dataset from the CSI 2014 Workshop with 6 metrics: location error (1.69 ± 0.78 mm), detection rate (100%) for vertebrae localization; the dice coefficient (0.953 ± 0.014), intersection over union (0.911 ± 0.025), Hausdorff distance (4.013 ± 2.128 mm), pixel accuracy (0.998 ± 0.001) for vertebrae segmentation. The experimental results demonstrated the efficiency of the proposed method. Furthermore, evaluation on the dataset from the xVertSeg challenge with location error (4.12 ± 2.31), detection rate (100%), dice coefficient (0.877 ± 0.035) shows the generalizability of our method. In summary, our solution localized the vertebrae successfully by detecting the centroids of vertebrae and implemented instance segmentation of vertebrae in the whole spine.

scholarly journals Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN

Informatics ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 40
Author(s):  
Nicola Altini ◽  
Giuseppe De Giosa ◽  
Nicola Fragasso ◽  
Claudia Coscia ◽  
Elena Sibilano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Ct Scans ◽  
Computer Assisted ◽  
Dice Coefficient ◽  
Labeling Algorithms ◽  
Two Phases ◽  
Vertebrae Segmentation ◽  
Whole Spine

The accurate segmentation and identification of vertebrae presents the foundations for spine analysis including fractures, malfunctions and other visual insights. The large-scale vertebrae segmentation challenge (VerSe), organized as a competition at the Medical Image Computing and Computer Assisted Intervention (MICCAI), is aimed at vertebrae segmentation and labeling. In this paper, we propose a framework that addresses the tasks of vertebrae segmentation and identification by exploiting both deep learning and classical machine learning methodologies. The proposed solution comprises two phases: a binary fully automated segmentation of the whole spine, which exploits a 3D convolutional neural network, and a semi-automated procedure that allows locating vertebrae centroids using traditional machine learning algorithms. Unlike other approaches, the proposed method comes with the added advantage of no requirement for single vertebrae-level annotations to be trained. A dataset of 214 CT scans has been extracted from VerSe’20 challenge data, for training, validating and testing the proposed approach. In addition, to evaluate the robustness of the segmentation and labeling algorithms, 12 CT scans from subjects affected by severe, moderate and mild scoliosis have been collected from a local medical clinic. On the designated test set from Verse’20 data, the binary spine segmentation stage allowed to obtain a binary Dice coefficient of 89.17%, whilst the vertebrae identification one reached an average multi-class Dice coefficient of 90.09%. In order to ensure the reproducibility of the algorithms hereby developed, the code has been made publicly available.

scholarly journals A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 131 ◽  
Author(s):  
Shimaa EL-Bana ◽  
Ahmad Al-Kabbany ◽  
Maha Sharkas
Keyword(s):  
Pulmonary Nodule ◽  
Semantic Segmentation ◽  
Ct Scans ◽  
Computational Time ◽  
Dice Coefficient ◽  
Two Stage ◽  
Second Stage ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
The Impact

This research is concerned with malignant pulmonary nodule detection (PND) in low-dose CT scans. Due to its crucial role in the early diagnosis of lung cancer, PND has considerable potential in improving the survival rate of patients. We propose a two-stage framework that exploits the ever-growing advances in deep neural network models, and that is comprised of a semantic segmentation stage followed by localization and classification. We employ the recently published DeepLab model for semantic segmentation, and we show that it significantly improves the accuracy of nodule detection compared to the classical U-Net model and its most recent variants. Using the widely adopted Lung Nodule Analysis dataset (LUNA16), we evaluate the performance of the semantic segmentation stage by adopting two network backbones, namely, MobileNet-V2 and Xception. We present the impact of various model training parameters and the computational time on the detection accuracy, featuring a 79.1% mean intersection-over-union (mIoU) and an 88.34% dice coefficient. This represents a mIoU increase of 60% and a dice coefficient increase of 30% compared to U-Net. The second stage involves feeding the output of the DeepLab-based semantic segmentation to a localization-then-classification stage. The second stage is realized using Faster RCNN and SSD, with an Inception-V2 as a backbone. On LUNA16, the two-stage framework attained a sensitivity of 96.4%, outperforming other recent models in the literature, including deep models. Finally, we show that adopting a transfer learning approach, particularly, the DeepLab model weights of the first stage of the framework, to infer binary (malignant-benign) labels on the Kaggle dataset for pulmonary nodules achieves a classification accuracy of 95.66%, which represents approximately 4% improvement over the recent literature.

Computer-Assisted Surgery in Craniomaxillofacial Surgery

Skull Base ◽  
2005 ◽  
Vol 15 (S 2) ◽  
Author(s):  
Ralf Gutwald ◽  
R. Schön ◽  
M. Metzger ◽  
C. Zizelmann ◽  
N.-C. Gellrich ◽  
...  
Keyword(s):  
Computer Assisted Surgery ◽  
Computer Assisted ◽  
Craniomaxillofacial Surgery

Accuracy and Practicability of Different Referencing Methods in Computer-Assisted Surgery of the Lateral Skull Base

Skull Base ◽  
2007 ◽  
Vol 16 (04) ◽  
Author(s):  
Klaus Stelter ◽  
Christoph Matthias ◽  
Kathrin Spiegl ◽  
Christian Lübbers ◽  
Andreas Leunig ◽  
...  
Keyword(s):  
Skull Base ◽  
Computer Assisted Surgery ◽  
Computer Assisted ◽  
Lateral Skull Base

Sonographically Aided Computer-Assisted Surgery at the Skull Base—Risks and Limitations

Skull Base ◽  
2007 ◽  
Vol 16 (04) ◽  
Author(s):  
Wolfgang Maier ◽  
Petra Lohnstein ◽  
Joerg Schipper
Keyword(s):  
Skull Base ◽  
Computer Assisted Surgery ◽  
Computer Assisted

An Enhanced Two Stage MMSE Equalizer for Coded FBMC/OQAM Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi
Keyword(s):  
Error Propagation ◽  
Channel Model ◽  
Error Correcting Codes ◽  
Two Stage ◽  
Probability Of Error ◽  
Second Stage ◽  
Different Types ◽  
Mmse Equalizer ◽  
And Performance ◽  
Ici Cancellation

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.

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