A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography

Oliveira, Bruno; Queirós, Sandro; Morais, Pedro; Torres, Helena R.; Gomes‐Fonseca, João; Fonseca, Jaime C.; Vilaça, João L.

doi:10.1016/j.media.2018.02.001

Cited by 31 publications

(17 citation statements)

References 26 publications

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“…The basic procedure that underlies multiatlas segmentation (MAS) methods [8]- [19] is the following: a number of images are selected from a dataset and registered onto the target structure, then corresponding annotations are transfered to the target and merged to localize and segment it. An atlas designates the pair of the intensity image and its annotation.…”

Section: A Multiorgan Segmentation Methodsmentioning

confidence: 99%

Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut

Kéchichian

Valette

Desvignes

2018

IEEE Trans. Med. Imaging

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We propose an automatic multiorgan segmentation method for 3-D radiological images of different anatomical contents and modalities. The approach is based on a simultaneous multilabel graph cut optimization of location, appearance, and spatial configuration criteria of target structures. Organ location is defined by target-specific probabilistic atlases (PA) constructed from a training dataset using a fast (2+1)D SURF-based multiscale registration method involving a simple four-parameter transformation. PAs are also used to derive target-specific organ appearance models represented as intensity histograms. The spatial configuration prior is derived from shortest-path constraints defined on the adjacency graph of structures. Thorough evaluations on Visceral project benchmarks and training dataset, as well as comparisons with the state-of-the-art confirm that our approach is comparable to and often outperforms similar approaches in multiorgan segmentation, thus proving that the combination of multiple suboptimal but complementary information sources can yield very good performance.

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Section: A Multiorgan Segmentation Methodsmentioning

confidence: 99%

Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut

Kéchichian

Valette

Desvignes

2018

IEEE Trans. Med. Imaging

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“…Atlases have recently been involved in several medical imaging problems, including the segmentation of brain tissues and lesions [1], [2], [3], prostate [4], lung [5], cardiac structures (e.g. myocardium) [6], [7], pancreas [8], [9], bones [10], cartilage [11], and multiple abdominal organ [12]. Atlases are used in segmentation problems based on image data originating from virtually all imaging modalities, including magnetic resonance images (MRI) [1], [2], [4], computed tomography (CT) [5], [9], [10], CT angiography [6], positron emission tomography (PET) [7], X-ray [13] and mammography [14].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

Fulop

Győrfi

Suranyi

et al. 2020

2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

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Atlases are frequently employed to assist medical image segmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores.

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“…When computer models are used to simulate physiological phenomena, explore pathogenesis, and design personalized surgery, image segmentation is an essential step for reconstructing the anatomical structure of relevant tissues and organs [2][3][4] . Some typical segmentation technologies, such as the active contour model [5][6][7][8][9][10] , atlas-based registration [11][12][13][14] , and neural network-based segmentation [15][16][17][18] , have become more mature over the past several decades. Other strategies, such as fuzzy clustering 19 , the superpixel method 20,21 , and graph-cut method 22,23 , are also well applied to medical image segmentation.…”

mentioning

confidence: 99%

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Huang

et al. 2020

Nat Commun

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Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.

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A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography

Cited by 31 publications

References 26 publications

Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut

Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut

Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

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