936 research outputs found
Deep learning for image-based liver analysis โ A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio
The optimal connection model for blood vessels segmentation and the MEA-Net
Vascular diseases have long been regarded as a significant health concern.
Accurately detecting the location, shape, and afflicted regions of blood
vessels from a diverse range of medical images has proven to be a major
challenge. Obtaining blood vessels that retain their correct topological
structures is currently a crucial research issue. Numerous efforts have sought
to reinforce neural networks' learning of vascular geometric features,
including measures to ensure the correct topological structure of the
segmentation result's vessel centerline. Typically, these methods extract
topological features from the network's segmentation result and then apply
regular constraints to reinforce the accuracy of critical components and the
overall topological structure. However, as blood vessels are three-dimensional
structures, it is essential to achieve complete local vessel segmentation,
which necessitates enhancing the segmentation of vessel boundaries.
Furthermore, current methods are limited to handling 2D blood vessel
fragmentation cases. Our proposed boundary attention module directly extracts
boundary voxels from the network's segmentation result. Additionally, we have
established an optimal connection model based on minimal surfaces to determine
the connection order between blood vessels. Our method achieves
state-of-the-art performance in 3D multi-class vascular segmentation tasks, as
evidenced by the high values of Dice Similarity Coefficient (DSC) and
Normalized Surface Dice (NSD) metrics. Furthermore, our approach improves the
Betti error, LR error, and BR error indicators of vessel richness and
structural integrity by more than 10% compared to other methods, and
effectively addresses vessel fragmentation and yields blood vessels with a more
precise topological structure.Comment: 19 page
๋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
Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas
Background: We sought to evaluate a new regional segmentation method for use with three-dimensional (3D) non-contrast abdominal CT images and to report the preliminary results. Methods: The proposed method was evaluated in ten cases. Manually segmented areas were used as the gold standard for evaluation. To compare the standard and the extracted liver regions, the degree of coincidence R% was redefined by transforming a volumetric overlap error. We also evaluated the influence of varying the density window size in terms of setting the starting points. Results: We confirmed in ten cases that our method could segment the liver region more precisely than the conventional method. A size of window 15 voxels was optimal as the starting point in all cases. Conclusions: We demonstrated the accuracy of a 3D semiautomatic liver segmentation method for non-contrast CT. This method promises to offer radiologists a time-efficient segmentation aid.Yamaguchi S., Satake K., Yamaji Y., et al. Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas. Computers in Biology and Medicine 55, 79 (2014); https://doi.org/10.1016/j.compbiomed.2014.10.003
Label-Free Liver Tumor Segmentation
We demonstrate that AI models can accurately segment liver tumors without the
need for manual annotation by using synthetic tumors in CT scans. Our synthetic
tumors have two intriguing advantages: (I) realistic in shape and texture,
which even medical professionals can confuse with real tumors; (II) effective
for training AI models, which can perform liver tumor segmentation similarly to
the model trained on real tumors -- this result is exciting because no existing
work, using synthetic tumors only, has thus far reached a similar or even close
performance to real tumors. This result also implies that manual efforts for
annotating tumors voxel by voxel (which took years to create) can be
significantly reduced in the future. Moreover, our synthetic tumors can
automatically generate many examples of small (or even tiny) synthetic tumors
and have the potential to improve the success rate of detecting small liver
tumors, which is critical for detecting the early stages of cancer. In addition
to enriching the training data, our synthesizing strategy also enables us to
rigorously assess the AI robustness.Comment: CVPR 202
Accurate geometry reconstruction of vascular structures using implicit splines
3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
An increasing number of public datasets have shown a marked impact on
automated organ segmentation and tumor detection. However, due to the small
size and partially labeled problem of each dataset, as well as a limited
investigation of diverse types of tumors, the resulting models are often
limited to segmenting specific organs/tumors and ignore the semantics of
anatomical structures, nor can they be extended to novel domains. To address
these issues, we propose the CLIP-Driven Universal Model, which incorporates
text embedding learned from Contrastive Language-Image Pre-training (CLIP) to
segmentation models. This CLIP-based label encoding captures anatomical
relationships, enabling the model to learn a structured feature embedding and
segment 25 organs and 6 types of tumors. The proposed model is developed from
an assembly of 14 datasets, using a total of 3,410 CT scans for training and
then evaluated on 6,162 external CT scans from 3 additional datasets. We rank
first on the Medical Segmentation Decathlon (MSD) public leaderboard and
achieve state-of-the-art results on Beyond The Cranial Vault (BTCV).
Additionally, the Universal Model is computationally more efficient (6x faster)
compared with dataset-specific models, generalized better to CT scans from
varying sites, and shows stronger transfer learning performance on novel tasks.Comment: Rank first in Medical Segmentation Decathlon (MSD) Competitio
Human treelike tubular structure segmentation: A comprehensive review and future perspectives
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed
A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning
Three-Dimensional (3-D) imaging is vital in computer-assisted surgical planning including minimal invasive surgery, targeted drug delivery, and tumor resection. Selective Internal Radiation Therapy (SIRT) is a liver directed radiation therapy for the treatment of liver cancer. Accurate calculation of anatomical liver and tumor volumes are essential for the determination of the tumor to normal liver ratio and for the calculation of the dose of Y-90 microspheres that will result in high concentration of the radiation in the tumor region as compared to nearby healthy tissue. Present manual techniques for segmentation of the liver from Computed Tomography (CT) tend to be tedious and greatly dependent on the skill of the technician/doctor performing the task.
This dissertation presents the development and implementation of a fully integrated algorithm for 3-D liver and tumor segmentation from tri-phase CT that yield highly accurate estimations of the respective volumes of the liver and tumor(s). The algorithm as designed requires minimal human intervention without compromising the accuracy of the segmentation results. Embedded within this algorithm is an effective method for extracting blood vessels that feed the tumor(s) in order to plan effectively the appropriate treatment.
Segmentation of the liver led to an accuracy in excess of 95% in estimating liver volumes in 20 datasets in comparison to the manual gold standard volumes. In a similar comparison, tumor segmentation exhibited an accuracy of 86% in estimating tumor(s) volume(s). Qualitative results of the blood vessel segmentation algorithm demonstrated the effectiveness of the algorithm in extracting and rendering the vasculature structure of the liver. Results of the parallel computing process, using a single workstation, showed a 78% gain. Also, statistical analysis carried out to determine if the manual initialization has any impact on the accuracy showed user initialization independence in the results.
The dissertation thus provides a complete 3-D solution towards liver cancer treatment planning with the opportunity to extract, visualize and quantify the needed statistics for liver cancer treatment. Since SIRT requires highly accurate calculation of the liver and tumor volumes, this new method provides an effective and computationally efficient process required of such challenging clinical requirements
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