Automated measurement of anterior and posterior acetabular sector angles

Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

doi:10.1117/12.910873

Cited by 4 publications

(7 citation statements)

References 24 publications

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“…as points that are clearly visible in the image and/or as points representing curvature extrema, terminal and centerline intersection points (Rohr, 2001). Well-defined landmarks simultaneously satisfy several of the above mentioned conditions and mark anatomically meaningful points (Lu et al, 2009, Proctor et al, 2010, Liu et al, 2010, Donner et al, 2013), object corners and centers (Ibragimov et al, 2012a), and evenly cover object boundaries (Stegmann et al, 2003, van Ginneken et al, 2006). In the present work, all types of landmarks were used, namely anatomically meaningful points (e.g.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of tongue muscles from super-resolution magnetic resonance images

Ibragimov

Prince

Murano

et al. 2015

Medical Image Analysis

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Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and swallowing problems.

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Section: Discussionmentioning

confidence: 99%

Segmentation of tongue muscles from super-resolution magnetic resonance images

Ibragimov

Prince

Murano

et al. 2015

Medical Image Analysis

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“…For this aim, we automatically detect the patient's head, which is used as a reference point for approximation of OAR positions. Following the geometry of the human head, we observe that the image gradients computed at skull boundaries are, in general, oriented toward the brain center, which allow us to apply the existing algorithm developed for detection of spherical structures, such as femoral heads . For every voxel

boldx

with gradient magnitude

false| normal∇ I_{x} false|

above a certain threshold, indicating that voxel x may belong to skull surface, we compute both positive

\nabla I_{boldx}

and negative

- \nabla I_{boldx}

gradient vectors.…”

Section: Methodsmentioning

confidence: 99%

“…Following the geometry of the human head, we observe that the image gradients computed at skull boundaries are, in general, oriented toward the brain center, which allow us to apply the existing algorithm developed for detection of spherical structures, such as femoral heads. 55 For every voxel x with gradient magnitude jrI x j above a certain threshold, indicating that voxel x may belong to skull surface, we compute both positiverI x and negative ÀrI x gradient vectors. A high number of these gradient vectors intersect around the center of the skull, as a high number of gradients rI x and ÀrI x are oriented toward the head center ( Fig.…”

Section: B Detection Of Organs-at-risksmentioning

confidence: 99%

Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks

2017

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Purpose Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software and inter-observer variability. Methods Convolutional neural networks (CNNs) – a concept from the field of deep learning – were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remov cavities of the component, which resulted in segmentation of the OAR in the test image. Results The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial software reported in the literature, and observed that CNNs demonstrate similar or superior performance on segmentation of spinal cord, mandible, parotid glands, larynx, pharynx, eye globes and optic nerves, but inferior performance on segmentation of submandibular glands and optic chiasm. Conclusion We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, e.g. MR images, may be beneficial for some OARs with poorly-visible boundaries.

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“…Although PV is a highly flexible object with no predefined shape, its anatomy, thickness and local tubularity can be used to distinguish PV from the surrounding structures with similar appearance. We detected the PV centerline from CNN-based enhancement by modifying the algorithm for vertebral body [42], femoral head [43] and human brain detection [49]. The resulting framework was successfully validated on a database of CT images of patients who underwent liver SBRT.…”

Section: Discussionmentioning

confidence: 99%

“…We adopt the algorithm for center and centerline detection of anatomical structures that can be approximated with solids of revolution, e.g. vertebral bodies [42] and femoral heads [43]. Being a composition of tubular structures, a ray initialized at a PV surface point y and oriented according to the surface normal g y will meet another surface point z with normal g z oriented in the opposite direction | g y ∙ g z | ≈ −1 (Fig.…”

Section: Methodsmentioning

confidence: 99%

Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

et al. 2017

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Automated segmentation of portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated PV segmentation from computed tomography (CT) images. We apply convolutional neural networks (CNN) to learn consistent appearance patterns of PV using a training set of CT images with reference annotations and then enhance PV in previously unseen CT images. Markov Random Fields (MRF) were further used to smooth the CNN enhancement results and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled to liver stereotactic body radiation therapy. The obtained segmentation accuracy was DSC = 0.83 and η = 1.08 mm in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNN and anatomy analysis can be used for accurate segmentation of PV and potentially integrated into liver radiation therapy planning.

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Automated measurement of anterior and posterior acetabular sector angles

Cited by 4 publications

References 24 publications

Segmentation of tongue muscles from super-resolution magnetic resonance images

Segmentation of tongue muscles from super-resolution magnetic resonance images

Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks

Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

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