537 research outputs found
Hepatic Vessel Segmentation for 3D Planning of Liver Surgery
none8Rationale and Objectives: The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic
segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver
surgery.
Materials and Methods: A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to
a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic
data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy
was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the
automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features.
Results: The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the
range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and
manually segmented vessel sections, with an average difference of 0.06 mm2. The average values of corresponding false-positive and
false-negative ratios were 7.7% and 2.3%, respectively.
Conclusions: A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed
tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel
delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative
resections.Francesco Conversano;Roberto Franchini;Christian Demitri;Laurent Massoptier;Francesco Montagna;Alfonso Maffezzoli;Antonio Malvasi;Sergio CasciaroFrancesco, Conversano; Roberto, Franchini; Demitri, Christian; Laurent, Massoptier; Francesco, Montagna; Maffezzoli, Alfonso; Antonio, Malvasi; Sergio, Casciar
A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning
Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ยฑ 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients
Liver Segmentation and its Application to Hepatic Interventions
The thesis addresses the development of an intuitive and accurate liver segmentation approach, its integration into software prototypes for the planning of liver interventions, and research on liver regeneration. The developed liver segmentation approach is based on a combination of the live wire paradigm and shape-based interpolation. Extended with two correction modes and integrated into a user-friendly workflow, the method has been applied to more than 5000 data sets. The combination of the liver segmentation with image analysis of hepatic vessels and tumors allows for the computation of anatomical and functional remnant liver volumes. In several projects with clinical partners world-wide, the benefit of the computer-assisted planning was shown. New insights about the postoperative liver function and regeneration could be gained, and most recent investigations into the analysis of MRI data provide the option to further improve hepatic intervention planning
Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology
Liver surgical resection is one of the most frequently used curative therapies. However,
resectability is problematic. There is a need for a computer-assisted surgical planning and
simulation system which can accurately and efficiently simulate the liver, vessels and
tumours in actual patients. The present project describes the development of these core
segmentation and deformable modelling techniques.
For precise detection of irregularly shaped areas with indistinct boundaries, the
segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets.
To improve efficiency, a chessboard distance transform was used to replace part of the GVF
effort. To automatically initialize the liver volume detection process, a rotating template was
introduced to locate the starting slice. For shape maintenance during the segmentation
process, a simplified object shape learning step was introduced to avoid occasional
significant errors. Skeletonization with fuzzy connectedness was used for vessel
segmentation.
To achieve real-time interactivity, the deformation regime of this system was based
on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh
refinement to raise the deformation accuracy and the mesh control quality. This method was
now extended to a multiple soft-tissue constraint system, by supplementing it with an
adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide
comparison of classic measures. Adjustable feature and parameter settings were thus
provided, to make tissues of interest distinct from adjacent structures, keeping the mesh
suitable for on-line topological transformation and deformation.
More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver
datasets were tested to evaluate the performance of the segmentation method. Instrument
manipulations of probing, grasping, and simple cutting were successfully simulated on
deformable constraint liver tissue models. This project was implemented in conjunction with
the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was
judged satisfactory by the consultant hepatic surgeon
RFA Guardian: Comprehensive Simulation of Radiofrequency Ablation Treatment of Liver Tumors
The RFA Guardian is a comprehensive application for high-performance patient-specific simulation of radiofrequency ablation of liver tumors. We address a wide range of usage scenarios. These include pre-interventional planning, sampling of the parameter space for uncertainty estimation, treatment evaluation and, in the worst case, failure analysis. The RFA Guardian is the first of its kind that exhibits sufficient performance for simulating treatment outcomes during the intervention. We achieve this by combining a large number of high-performance image processing, biomechanical simulation and visualization techniques into a generalized technical workflow. Further, we wrap the feature set into a single, integrated application, which exploits all available resources of standard consumer hardware, including massively parallel computing on graphics processing units. This allows us to predict or reproduce treatment outcomes on a single personal computer with high computational performance and high accuracy. The resulting low demand for infrastructure enables easy and cost-efficient integration into the clinical routine. We present a number of evaluation cases from the clinical practice where users performed the whole technical workflow from patient-specific modeling to final validation and highlight the opportunities arising from our fast, accurate prediction techniques
๋ณต๋ถ CT์์ ๊ฐ๊ณผ ํ๊ด ๋ถํ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ์ ์๊ธธ.๋ณต๋ถ ์ ์ฐํ ๋จ์ธต ์ดฌ์ (CT) ์์์์ ์ ํํ ๊ฐ ๋ฐ ํ๊ด ๋ถํ ์ ์ฒด์ ์ธก์ , ์น๋ฃ ๊ณํ ์๋ฆฝ ๋ฐ ์ถ๊ฐ์ ์ธ ์ฆ๊ฐ ํ์ค ๊ธฐ๋ฐ ์์ ๊ฐ์ด๋์ ๊ฐ์ ์ปดํจํฐ ์ง๋จ ๋ณด์กฐ ์์คํ
์ ๊ตฌ์ถํ๋๋ฐ ํ์์ ์ธ ์์์ด๋ค. ์ต๊ทผ ๋ค์ด ์ปจ๋ณผ๋ฃจ์
๋ ์ธ๊ณต ์ ๊ฒฝ๋ง (CNN) ํํ์ ๋ฅ ๋ฌ๋์ด ๋ง์ด ์ ์ฉ๋๋ฉด์ ์๋ฃ ์์ ๋ถํ ์ ์ฑ๋ฅ์ด ํฅ์๋๊ณ ์์ง๋ง, ์ค์ ์์์ ์ ์ฉํ ์ ์๋ ๋์ ์ผ๋ฐํ ์ฑ๋ฅ์ ์ ๊ณตํ๊ธฐ๋ ์ฌ์ ํ ์ด๋ ต๋ค. ๋ํ ๋ฌผ์ฒด์ ๊ฒฝ๊ณ๋ ์ ํต์ ์ผ๋ก ์์ ๋ถํ ์์ ๋งค์ฐ ์ค์ํ ์์๋ก ์ด์ฉ๋์์ง๋ง, CT ์์์์ ๊ฐ์ ๋ถ๋ถ๋ช
ํ ๊ฒฝ๊ณ๋ฅผ ์ถ์ถํ๊ธฐ๊ฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ ํ๋ CNN์์๋ ์ด๋ฅผ ์ฌ์ฉํ์ง ์๊ณ ์๋ค. ๊ฐ ํ๊ด ๋ถํ ์์
์ ๊ฒฝ์ฐ, ๋ณต์กํ ํ๊ด ์์์ผ๋ก๋ถํฐ ํ์ต ๋ฐ์ดํฐ๋ฅผ ๋ง๋ค๊ธฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ ๋ฅ ๋ฌ๋์ ์ ์ฉํ๊ธฐ๊ฐ ์ด๋ ต๋ค. ๋ํ ์์ ํ๊ด ๋ถ๋ถ์ ์์ ๋ฐ๊ธฐ ๋๋น๊ฐ ์ฝํ์ฌ ์๋ณธ ์์์์ ์๋ณํ๊ธฐ๊ฐ ๋งค์ฐ ์ด๋ ต๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ ์ธ๊ธํ ๋ฌธ์ ๋ค์ ํด๊ฒฐํ๊ธฐ ์ํด ์ผ๋ฐํ ์ฑ๋ฅ์ด ํฅ์๋ CNN๊ณผ ์์ ํ๊ด์ ํฌํจํ๋ ๋ณต์กํ ๊ฐ ํ๊ด์ ์ ํํ๊ฒ ๋ถํ ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค.
๊ฐ ๋ถํ ์์
์์ ์ฐ์ํ ์ผ๋ฐํ ์ฑ๋ฅ์ ๊ฐ๋ CNN์ ๊ตฌ์ถํ๊ธฐ ์ํด, ๋ด๋ถ์ ์ผ๋ก ๊ฐ ๋ชจ์์ ์ถ์ ํ๋ ๋ถ๋ถ์ด ํฌํจ๋ ์๋ ์ปจํ
์คํธ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๋ํ, CNN์ ์ฌ์ฉํ ํ์ต์ ๊ฒฝ๊ณ์ ์ ๊ฐ๋
์ด ์๋กญ๊ฒ ์ ์๋๋ค. ๋ชจํธํ ๊ฒฝ๊ณ๋ถ๊ฐ ํฌํจ๋์ด ์์ด ์ ์ฒด ๊ฒฝ๊ณ ์์ญ์ CNN์ ํ๋ จํ๋ ๊ฒ์ ๋งค์ฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ ๋ฐ๋ณต๋๋ ํ์ต ๊ณผ์ ์์ ์ธ๊ณต ์ ๊ฒฝ๋ง์ด ์ค์ค๋ก ์์ธกํ ํ๋ฅ ์์ ๋ถ์ ํํ๊ฒ ์ถ์ ๋ ๋ถ๋ถ์ ๊ฒฝ๊ณ๋ง์ ์ฌ์ฉํ์ฌ ์ธ๊ณต ์ ๊ฒฝ๋ง์ ํ์ตํ๋ค. ์คํ์ ๊ฒฐ๊ณผ๋ฅผ ํตํด ์ ์๋ CNN์ด ๋ค๋ฅธ ์ต์ ๊ธฐ๋ฒ๋ค๋ณด๋ค ์ ํ๋๊ฐ ์ฐ์ํ๋ค๋ ๊ฒ์ ๋ณด์ธ๋ค. ๋ํ, ์ ์๋ CNN์ ์ผ๋ฐํ ์ฑ๋ฅ์ ๊ฒ์ฆํ๊ธฐ ์ํด ๋ค์ํ ์คํ์ ์ํํ๋ค.
๊ฐ ํ๊ด ๋ถํ ์์๋ ๊ฐ ๋ด๋ถ์ ๊ด์ฌ ์์ญ์ ์ง์ ํ๊ธฐ ์ํด ์์ ํ๋ํ ๊ฐ ์์ญ์ ํ์ฉํ๋ค. ์ ํํ ๊ฐ ํ๊ด ๋ถํ ์ ์ํด ํ๊ด ํ๋ณด ์ ๋ค์ ์ถ์ถํ์ฌ ์ฌ์ฉํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ํ์คํ ํ๋ณด ์ ๋ค์ ์ป๊ธฐ ์ํด, ์ผ์ฐจ์ ์์์ ์ฐจ์์ ๋จผ์ ์ต๋ ๊ฐ๋ ํฌ์ ๊ธฐ๋ฒ์ ํตํด ์ด์ฐจ์์ผ๋ก ๋ฎ์ถ๋ค. ์ด์ฐจ์ ์์์์๋ ๋ณต์กํ ํ๊ด์ ๊ตฌ์กฐ๊ฐ ๋ณด๋ค ๋จ์ํ๋ ์ ์๋ค. ์ด์ด์, ์ด์ฐจ์ ์์์์ ํ๊ด ๋ถํ ์ ์ํํ๊ณ ํ๊ด ํฝ์
๋ค์ ์๋์ ์ผ์ฐจ์ ๊ณต๊ฐ์์ผ๋ก ์ญ ํฌ์๋๋ค. ๋ง์ง๋ง์ผ๋ก, ์ ์ฒด ํ๊ด์ ๋ถํ ์ ์ํด ์๋ณธ ์์๊ณผ ํ๊ด ํ๋ณด ์ ๋ค์ ๋ชจ๋ ์ฌ์ฉํ๋ ์๋ก์ด ๋ ๋ฒจ ์
๊ธฐ๋ฐ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ๋ณต์กํ ๊ตฌ์กฐ๊ฐ ๋จ์ํ๋๊ณ ์์ ํ๊ด์ด ๋ ์ ๋ณด์ด๋ ์ด์ฐจ์ ์์์์ ์ป์ ํ๋ณด ์ ๋ค์ ์ฌ์ฉํ๊ธฐ ๋๋ฌธ์ ์์ ํ๊ด ๋ถํ ์์ ๋์ ์ ํ๋๋ฅผ ๋ณด์ธ๋ค. ์คํ์ ๊ฒฐ๊ณผ์ ์ํ๋ฉด ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ์๋ชป๋ ์์ญ์ ์ถ์ถ ์์ด ๋ค๋ฅธ ๋ ๋ฒจ ์
๊ธฐ๋ฐ ์๊ณ ๋ฆฌ์ฆ๋ค๋ณด๋ค ์ฐ์ํ ์ฑ๋ฅ์ ๋ณด์ธ๋ค.
์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๊ณผ ํ๊ด์ ๋ถํ ํ๋ ์๋ก์ด ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ ์๋ ์๋ ์ปจํ
์คํธ ๊ตฌ์กฐ๋ ์ฌ๋์ด ๋์์ธํ ํ์ต ๊ณผ์ ์ด ์ผ๋ฐํ ์ฑ๋ฅ์ ํฌ๊ฒ ํฅ์ํ ์ ์๋ค๋ ๊ฒ์ ๋ณด์ธ๋ค. ๊ทธ๋ฆฌ๊ณ ์ ์๋ ๊ฒฝ๊ณ์ ํ์ต ๊ธฐ๋ฒ์ผ๋ก CNN์ ์ฌ์ฉํ ์์ ๋ถํ ์ ์ฑ๋ฅ์ ํฅ์ํ ์ ์์์ ๋ดํฌํ๋ค. ๊ฐ ํ๊ด์ ๋ถํ ์ ์ด์ฐจ์ ์ต๋ ๊ฐ๋ ํฌ์ ๊ธฐ๋ฐ ์ด๋ฏธ์ง๋ก๋ถํฐ ํ๋๋ ํ๊ด ํ๋ณด ์ ๋ค์ ํตํด ์์ ํ๊ด๋ค์ด ์ฑ๊ณต์ ์ผ๋ก ๋ถํ ๋ ์ ์์์ ๋ณด์ธ๋ค. ๋ณธ ๋
ผ๋ฌธ์์ ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ์ ํด๋ถํ์ ๋ถ์๊ณผ ์๋ํ๋ ์ปดํจํฐ ์ง๋จ ๋ณด์กฐ ์์คํ
์ ๊ตฌ์ถํ๋ ๋ฐ ๋งค์ฐ ์ค์ํ ๊ธฐ์ ์ด๋ค.Accurate liver and its vessel segmentation on abdominal computed tomography (CT) images is one of the most important prerequisites for computer-aided diagnosis (CAD) systems such as volumetric measurement, treatment planning, and further augmented reality-based surgical guide. In recent years, the application of deep learning in the form of convolutional neural network (CNN) has improved the performance of medical image segmentation, but it is difficult to provide high generalization performance for the actual clinical practice. Furthermore, although the contour features are an important factor in the image segmentation problem, they are hard to be employed on CNN due to many unclear boundaries on the image. In case of a liver vessel segmentation, a deep learning approach is impractical because it is difficult to obtain training data from complex vessel images. Furthermore, thin vessels are hard to be identified in the original image due to weak intensity contrasts and noise. In this dissertation, a CNN with high generalization performance and a contour learning scheme is first proposed for liver segmentation. Secondly, a liver vessel segmentation algorithm is presented that accurately segments even thin vessels.
To build a CNN with high generalization performance, the auto-context algorithm is employed. The auto-context algorithm goes through two pipelines: the first predicts the overall area of a liver and the second predicts the final liver using the first prediction as a prior. This process improves generalization performance because the network internally estimates shape-prior. In addition to the auto-context, a contour learning method is proposed that uses only sparse contours rather than the entire contour. Sparse contours are obtained and trained by using only the mispredicted part of the network's final prediction. Experimental studies show that the proposed network is superior in accuracy to other modern networks. Multiple N-fold tests are also performed to verify the generalization performance.
An algorithm for accurate liver vessel segmentation is also proposed by introducing vessel candidate points. To obtain confident vessel candidates, the 3D image is first reduced to 2D through maximum intensity projection. Subsequently, vessel segmentation is performed from the 2D images and the segmented pixels are back-projected into the original 3D space. Finally, a new level set function is proposed that utilizes both the original image and vessel candidate points. The proposed algorithm can segment thin vessels with high accuracy by mainly using vessel candidate points. The reliability of the points can be higher through robust segmentation in the projected 2D images where complex structures are simplified and thin vessels are more visible. Experimental results show that the proposed algorithm is superior to other active contour models.
The proposed algorithms present a new method of segmenting the liver and its vessels. The auto-context algorithm shows that a human-designed curriculum (i.e., shape-prior learning) can improve generalization performance. The proposed contour learning technique can increase the accuracy of a CNN for image segmentation by focusing on its failures, represented by sparse contours. The vessel segmentation shows that minor vessel branches can be successfully segmented through vessel candidate points obtained by reducing the image dimension. The algorithms presented in this dissertation can be employed for later analysis of liver anatomy that requires accurate segmentation techniques.Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Problem statement 3
1.3 Main contributions 6
1.4 Contents and organization 9
Chapter 2 Related Works 10
2.1 Overview 10
2.2 Convolutional neural networks 11
2.2.1 Architectures of convolutional neural networks 11
2.2.2 Convolutional neural networks in medical image segmentation 21
2.3 Liver and vessel segmentation 37
2.3.1 Classical methods for liver segmentation 37
2.3.2 Vascular image segmentation 40
2.3.3 Active contour models 46
2.3.4 Vessel topology-based active contour model 54
2.4 Motivation 60
Chapter 3 Liver Segmentation via Auto-Context Neural Network with Self-Supervised Contour Attention 62
3.1 Overview 62
3.2 Single-pass auto-context neural network 65
3.2.1 Skip-attention module 66
3.2.2 V-transition module 69
3.2.3 Liver-prior inference and auto-context 70
3.2.4 Understanding the network 74
3.3 Self-supervising contour attention 75
3.4 Learning the network 81
3.4.1 Overall loss function 81
3.4.2 Data augmentation 81
3.5 Experimental Results 83
3.5.1 Overview 83
3.5.2 Data configurations and target of comparison 84
3.5.3 Evaluation metric 85
3.5.4 Accuracy evaluation 87
3.5.5 Ablation study 93
3.5.6 Performance of generalization 110
3.5.7 Results from ground-truth variations 114
3.6 Discussion 116
Chapter 4 Liver Vessel Segmentation via Active Contour Model with Dense Vessel Candidates 119
4.1 Overview 119
4.2 Dense vessel candidates 124
4.2.1 Maximum intensity slab images 125
4.2.2 Segmentation of 2D vessel candidates and back-projection 130
4.3 Clustering of dense vessel candidates 135
4.3.1 Virtual gradient-assisted regional ACM 136
4.3.2 Localized regional ACM 142
4.4 Experimental results 145
4.4.1 Overview 145
4.4.2 Data configurations and environment 146
4.4.3 2D segmentation 146
4.4.4 ACM comparisons 149
4.4.5 Evaluation of bifurcation points 154
4.4.6 Computational performance 159
4.4.7 Ablation study 160
4.4.8 Parameter study 162
4.5 Application to portal vein analysis 164
4.6 Discussion 168
Chapter 5 Conclusion and Future Works 170
Bibliography 172
์ด๋ก 197Docto
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