Automatic Lung Tumor Segmentation with Leaks Removal in Follow-up CT Studies

Vivanti, Refael; Karaaslan, Onur; Joskowicz, Leo

doi:10.1007/978-3-319-13909-8_12

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The main contribution is additional dividing into subclasses, what lets significantly (by 39% VOE) increase segmentation quality. Thus, we have the quality that is comparable with analogous works [6]. The objective of future researches is to create and develop methods for automatic dividing classes into subclasses, for instance, clustering.…”

Section: Resultsmentioning

confidence: 82%

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Liver tumor segmentation CT data based on Alexnet-like convolution neural nets

Korabelnikov¹,

Колсанов²,

Сhaplygin³

et al. 2016

Proceedings of International Conference Information Technology and Nanotechnology (ITNT-2016)

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Abstract. Anatomical structure segmentation on computed tomography (CT) is the key stage in medical visualization and computer diagnosis. Tumors are one of types of internal structures, for which the problem of automatic segmentation today has no solution fully satisfying by quality. The reason is high variance of tumor's density and inability of using a priori anatomical information about shape. In this paper we propose automatic method of liver tumors segmentation based on convolution neural nets (CNN). Studying and validation have been performed on set of CT with liver and tumors segmentation ground truth. Average error (VOE) by cross-validation is 17.3%. Also there were considered algorithms of pre-and post-processing which increase accuracy and performance of segmentation procedure. Particularly the acceleration of the segmentation procedure with negligible decrease of quality has been reached 6 times.

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Section: Resultsmentioning

confidence: 82%

“…Image segmentation is a labeling each pixel of the image by common visual characteristics [6]. In this work, the image has divided into two segments: tumors and healthy liver tissues.…”

Section: Introductionmentioning

confidence: 99%

Liver tumor segmentation CT data based on Alexnet-like convolution neural nets

Korabelnikov¹,

Колсанов²,

Сhaplygin³

et al. 2016

Proceedings of International Conference Information Technology and Nanotechnology (ITNT-2016)

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“…Figure shows typical examples of lung‐boundary adhesion large tumors, where a large tumor remains confined to the damaged lung or if the ratio of the tumor area to the damaged lung area is much less than unity, it is categorized as a moderate large‐sized (ML) tumor; otherwise, it is categorized as a giant large‐sized (GL) tumor. In the past, numerous methods have been proposed to mainly segment on small lung masses attached to lung boundaries (e.g., juxta‐pleural nodules) including region‐growing or watershed followed by morphological processing, supervised segmentation models based on feature extraction and support vector machines or neural networks, and energy minimization models with prior knowledge . With the intensive investigation of sparse representation, the sparse constraints have been used to model shape priors, which explicitly model spatially contiguous gross errors (non‐Gaussian errors) in input shapes with sparse vectors and assume that these gross errors are sparse with respect to the given shape information .…”

Section: Introductionmentioning

confidence: 99%

Spline curve deformation model with prior shapes for identifying adhesion boundaries between large lung tumors and tissues around lungs in CT images

et al. 2020

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Purpose Automated segmentation of lung tumors attached to anatomic structures such as the chest wall or mediastinum remains a technical challenge because of the similar Hounsfield units of these structures. To address this challenge, we propose herein a spline curve deformation model that combines prior shapes to correct large spatially contiguous errors (LSCEs) in input shapes derived from image‐appearance cues.The model is then used to identify the adhesion boundaries between large lung tumors and tissue around the lungs. Methods The deformation of the whole curve is driven by the transformation of the control points (CPs) of the spline curve, which are influenced by external and internal forces. The external force drives the model to fit the positions of the non‐LSCEs of the input shapes while the internal force ensures the local similarity of the displacements of the neighboring CPs. The proposed model corrects the gross errors in the lung input shape caused by large lung tumors, where the initial lung shape for the model is inferred from the training shapes by shape group‐based sparse prior information and the input lung shape is inferred by adaptive‐thresholding‐based segmentation followed by morphological refinement. Results The accuracy of the proposed model is verified by applying it to images of lungs with either moderate large‐sized (ML) tumors or giant large‐sized (GL) tumors. The quantitative results in terms of the averages of the dice similarity coefficient (DSC) and the Jaccard similarity index (SI) are 0.982 ± 0.006 and 0.965 ± 0.012 for segmentation of lungs adhered by ML tumors, and 0.952 ± 0.048 and 0.926 ± 0.059 for segmentation of lungs adhered by GL tumors, which give 0.943 ± 0.021 and 0.897 ± 0.041 for segmentation of the ML tumors, and 0.907 ± 0.057 and 0.888 ± 0.091 for segmentation of the GL tumors, respectively. In addition, the bidirectional Hausdorff distances are 5.7 ± 1.4 and 11.3 ± 2.5 mm for segmentation of lungs with ML and GL tumors, respectively. Conclusions When combined with prior shapes, the proposed spline curve deformation can deal with large spatially consecutive errors in object shapes obtained from image‐appearance information. We verified this method by applying it to the segmentation of lungs with large tumors adhered to the tissue around the lungs and the large tumors. Both the qualitative and quantitative results are more accurate and repeatable than results obtained with current state‐of‐the‐art techniques.

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Robust Lesion Segmentation on Emission Computed Tomography with nmODE

Zhang,

Guo

2023

2023 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC)

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Automatic Lung Tumor Segmentation with Leaks Removal in Follow-up CT Studies

Cited by 3 publications

References 11 publications

Liver tumor segmentation CT data based on Alexnet-like convolution neural nets

Liver tumor segmentation CT data based on Alexnet-like convolution neural nets

Spline curve deformation model with prior shapes for identifying adhesion boundaries between large lung tumors and tissues around lungs in CT images

Robust Lesion Segmentation on Emission Computed Tomography with nmODE

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