Feature-based Non-rigid Registration between Pre- and Post-Contrast Lung CT Images

SummaryObjectives: Accurate registration of lung CT images is inevitable for numerous clinical applications. Usually, nonlinear intensity-based methods are used. Their accuracy is typically evaluated using corresponding anatomical points (landmarks; e.g. bifurcations of bronchial and vessel trees) annotated by medical experts in the images to register. As image registration can be interpreted as correspond ence finding problem, these corresponding landmarks can also be used in feature-based registration techniques. Recently, approaches for automated identification of such landmark correspondences in lung CT images have been presented. In this work, a novel combination of variational nonlinear intensity-based registration with an approach for automated landmark correspond ence detection in lung CT pairs is presented and evaluated.Methods: The main blocks of the proposed hybrid intensity- and feature-based registration scheme are a two-step landmark correspondence detection and the so-called CoLD (Combining Landmarks and Distance Measures) framework. The landmark correspondence identification starts with feature detection in one image followed by a blockmatching-based transfer of the features to the other image. The established correspond ences are used to compute a thin-plate spline (TPS) transformation. Within CoLD, the TPS transformation is improved by minimization of an objective function consisting of a Normalized Gradient Field distance measure and a curvature regularizer; the landmark correspondences are guaranteed to be preserved by optimization on the kernel of the discretized landmark constraints.Results: Based on ten publicly available end-inspiration/expiration CT scan pairs with anatomical landmark sets annotated by medical experts from the DIR-Lab database, it is shown that the hybrid registration approach is superior in terms of accuracy: The mean distance of expert landmarks is decreased from 8.46 mm before to 1.15 mm after registration, outperforming both the TPS transformation (1.68 mm) and a nonlinear registration without usage of automatically detected landmarks (2.44 mm). The improvement is statistically significant in eight of ten datasets in comparison to TPS and in nine of ten datasets in comparison to the intensity-based registration. Furthermore, CoLD globally estimates the breathing-induced lung volume change well and results in smooth and physiologically plausible motion fields of the lungs.Conclusions: We demonstrated that our novel landmark-based registration pipeline outperforms both TPS and the underlying nonlinear intensity-based registration without landmark usage. This highlights the potential of automatic landmark correspondence detection for improvement of lung CT registration accuracy.

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Multi-factorial considerations for intra-thoracic lymph node evaluations of healthy cats on computed tomographic images

BMC Veterinary Research ◽

10.1186/s12917-021-02771-7 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Ninlawan Thammasiri ◽

Chutimon Thanaboonnipat ◽

Nan Choisunirachon ◽

Damri Darawiroj

Keyword(s):

Lymph Node ◽

Lymph Nodes ◽

Ct Images ◽

Slice Thickness ◽

Maximum Width ◽

Post Contrast ◽

Computed Tomographic ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct

Abstract Background It is difficult to examine mild to moderate feline intra-thoracic lymphadenopathy via and thoracic radiography. Despite previous information from computed tomographic (CT) images of intra-thoracic lymph nodes, some factors from animals and CT setting were less elucidated. Therefore, this study aimed to investigate the effect of internal factors from animals and external factors from the CT procedure on the feasibility to detect the intra-thoracic lymph nodes. Twenty-four, client-owned, clinically healthy cats were categorized into three groups according to age. They underwent pre- and post-contrast enhanced CT for whole thorax followed by inter-group evaluation and comparison of sternal, cranial mediastinal, and tracheobronchial lymph nodes. Results Post contrast-enhanced CT appearances revealed that intra-thoracic lymph nodes of kittens were invisible, whereas the sternal, cranial mediastinal, and tracheobronchial nodes of cats aged over 7 months old were detected (6/24, 9/24 and 7/24, respectively). Maximum width of these lymph nodes were 3.93 ± 0.74 mm, 4.02 ± 0.65 mm, and 3.51 ± 0.62 mm, respectively. By age, lymph node sizes of these cats were not significantly different. Transverse lymph node width of males was larger than that of females (P = 0.0425). Besides, the detection score of lymph nodes was affected by slice thickness (P < 0.01) and lymph node width (P = 0.0049). Furthermore, an irregular, soft tissue structure, possibly the thymus, was detected in all juvenile cats and three mature cats. Conclusions Despite additional information on intra-thoracic lymph nodes in CT images, which can be used to investigate lymphatic-related abnormalities, age, sex, and slice thickness of CT images must be also considered.

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Feature based analyses of lung nodules from computed tomography (CT) images

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1020/1/012007 ◽

2021 ◽

Vol 1020 ◽

pp. 012007

Author(s):

Md. Anwar Hussain ◽

Lakshipriya Gogoi

Keyword(s):

Computed Tomography ◽

Ct Images ◽

Lung Nodules ◽

Feature Based

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A Feature-Based Learning Framework for Accurate Prostate Localization in CT Images

IEEE Transactions on Image Processing ◽

10.1109/tip.2012.2194296 ◽

2012 ◽

Vol 21 (8) ◽

pp. 3546-3559 ◽

Cited By ~ 22

Author(s):

Shu Liao ◽

Dinggang Shen

Keyword(s):

Ct Images ◽

Learning Framework ◽

Feature Based

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ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Sensors ◽

10.3390/s21010268 ◽

2021 ◽

Vol 21 (1) ◽

pp. 268

Author(s):

Yeganeh Jalali ◽

Mansoor Fateh ◽

Mohsen Rezvani ◽

Vahid Abolghasemi ◽

Mohammad Hossein Anisi

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Ct Images ◽

Medical Image Segmentation ◽

Great Success ◽

Morphological Operations ◽

Ct Image ◽

Feature Maps ◽

Learning Framework ◽

Lung Ct

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

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