Hashing-Based Atlas Ranking and Selection for Multiple-Atlas Segmentation

Katouzian, Amin; Wang, Hongzhi; Conjeti, Sailesh; Tang, Hui; Dehghan, Ehsan; Karargyris, Alexandros; Pillai, Anup; Clarkson, Kenneth L.; Navab, Nassir

doi:10.1007/978-3-030-00937-3_62

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…To compare the methods that required less than five minutes processing time, an MSD of 1.57 mm [28], 0.98 mm [30], and 2.2 to 8.6 mm [20] were reported. A Dice index from 0.77 to 0.90 [16], from 0.78 to 0.94 [20], from 0.84 to 0.93 [21], and from 0.67 to 0.96 [23] were also described. In a recent MICCAI-STACOM challenge [4], a Dice range from 0.81 to 0.91, an HD range from 25.2 to 55.4 mm, and an MSD range from 1.11 to 4.20 mm were summarized from the comparison of ten cardiac CTA segmentation algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…Zhuang et al [10] segmented those seven cardiac structures plus the DA. A more comprehensive coverage was shown in Zhou et al's [12] work for segmenting 15 structures, and in Katouzian et al [16] and Wang et al [18] for 16 structures. However, these works split the PA into left and right trunks, and the AA into aortic arch and root as separate structures.…”

Section: Previous Workmentioning

confidence: 94%

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Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

et al. 2020

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Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application. INDEX TERMS Computed tomography, heart segmentation, multi-atlas segmentation, random walk.

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Section: Discussionmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 94%

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

et al. 2020

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“…44 In Ref 36 the best segmentation quality was estimated by the extreme value theory under the assumption of a given large enough atlas set (up to 5000 atlases). Although larger atlas sets can contain more patterns for tolerating individual variations, computational burden caused by registration 47 could not be afforded in clinical practice. Moreover, the quality of the atlases fundamentally influences segmentation quality, 48 and therefore atlas selection 46,49 is introduced into the atlas-based segmentation methods to yield more accurate segmentation and to reduce the computational load of registration.…”

Section: Related Workmentioning

confidence: 99%

SOMA: Subject‐, object‐, and modality‐adapted precision atlas approach for automatic anatomy recognition and delineation in medical images

Udupa

Odhner

et al. 2021

Medical Physics

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In the multi-atlas segmentation (MAS) method, a large enough atlas set, which can cover the complete spectrum of the whole population pattern of the target object will benefit the segmentation quality. However, the difficulty in obtaining and generating such a large set of atlases and the computational burden required in the segmentation procedure make this approach impractical. In this paper, we propose a method called SOMA to select subject-, object-, and modality-adapted precision atlases for automatic anatomy recognition in medical images with pathology, following the idea that different regions of the target object in a novel image can be recognized by different atlases with regionally best similarity, so that effective atlases have no need to be globally similar to the target subject and also have no need to be overall similar to the target object. Methods: The SOMA method consists of three main components: atlas building, object recognition, and object delineation. Considering the computational complexity, we utilize an all-to-template strategy to align all images to the same image space belonging to the root image determined by the minimum spanning tree (MST) strategy among a subset of radiologically near-normal images. The object recognition process is composed of two stages: rough recognition and refined recognition. In rough recognition, subimage matching is conducted between the test image and each image of the whole atlas set, and only the atlas corresponding to the best-matched subimage contributes to the recognition map regionally. The frequency of best match for each atlas is recorded by a counter, and the atlases with the highest frequencies are selected as the precision atlases. In refined recognition, only the precision atlases are examined, and the subimage matching is conducted in a nonlocal manner of searching to further increase the accuracy of boundary matching. Delineation is based on a U-net-based deep learning network, where the original gray scale image together with the fuzzy map from refined recognition compose a two-channel input to the network, and the output is a segmentation map of the target object. Results: Experiments are conducted on computed tomography (CT) images with different qualities in two body regions -head and neck (H&N) and thorax, from 298 subjects with nine objects and 241 subjects with six objects, respectively. Most objects achieve a localization error within two voxels after refined recognition, with marked improvement in localization accuracy from rough to refined recognition of 0.6-3 mm in H&N and 0.8-4.9 mm in thorax, and also in delineation accuracy (Dice coefficient) from refined recognition to delineation of 0.01-0.11 in H&N and 0.01-0.18 in thorax. Conclusions:The SOMA method shows high accuracy and robustness in anatomy recognition and delineation. The improvements from rough to refined 7806

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“…In their piplines, CNNs are indirectly used to perform segmentation. These include using a CNN to infer deformation maps to register target images to atlas images [2] and using it to implicitly infer the segmentation masks [4,6]; using CNNs to improve the selection of best atlases from a library [10]. As this is not the focus of this paper, we will not discuss them further and will concentrate on methods that directly use the PAs to assist a CNN that perform segmentation.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Atlases to Enforce Topological Constraints

Wickramasinghe

Knott

Fua

2019

Lecture Notes in Computer Science

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Probabilistic atlases (PAs) have long been used in standard segmentation approaches and, more recently, in conjunction with Convolutional Neural Networks (CNNs). However, their use has been restricted to relatively standardized structures such as the brain or heart which have limited or predictable range of deformations. Here we propose an encoding-decoding CNN architecture that can exploit rough atlases that encode only the topology of the target structures that can appear in any pose and have arbitrarily complex shapes to improve the segmentation results. It relies on the output of the encoder to compute both the pose parameters used to deform the atlas and the segmentation mask itself, which makes it effective and end-to-end trainable.

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Hashing-Based Atlas Ranking and Selection for Multiple-Atlas Segmentation

Cited by 6 publications

References 15 publications

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

SOMA: Subject‐, object‐, and modality‐adapted precision atlas approach for automatic anatomy recognition and delineation in medical images

Probabilistic Atlases to Enforce Topological Constraints

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