Segmentation Based Features for Lymph Node Detection from 3-D Chest CT

Feulner, Johannes; Zhou, S. Kevin; Hammon, Matthias; Hornegger, Joachim; Comanicìu, Dorin

doi:10.1007/978-3-642-24319-6_12

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…In the second approach, known as the continuous convex relaxation method, the image is treated in a continuous domain, and the optimal labeling minimization problem, initially nonconvex, is relaxed to obtain an equivalent convex minimization problem, which is solved via continuous max-flow formulation. The scientific literature shows how these types of mathematical models have been used to segment different tumors, mainly in the lungs [4,26,27,42,44,48], liver [29], lymph nodes [5,13,16,17,54], prostate [20,25,31], brain [12] and breast [49]. Some studies used level-set and active contour methods [13,20,25,26,34,44,48], while others were based on the graph-cut method [4,7,12,16,17,27,31,42,54].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Suárez-Mejías

Pérez-Carrasco

Serrano

et al. 2016

Med Biol Eng Comput

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An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm's dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.

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Section: Introductionmentioning

confidence: 99%

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Suárez-Mejías

Pérez-Carrasco

Serrano

et al. 2016

Med Biol Eng Comput

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“…However, the process involves lack of efficiency in learning process prior to perform segmentation. Such problems of learning were addressed in the work of Feulner et al [23], [24] by using discriminative approach for segmenting lymph node. The authors have utilized graph cut algorithm for carrying out segmentation.…”

Section: Related Workmentioning

confidence: 99%

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Savitha¹

2019

IJECE

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Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.

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“…In [24], a graph cut with locally adaptive energy dealt with spatially varying distributions of LN parenchyma and fat caused by in-homogeneous acoustic attenuation. In [25], a segmenting blob-like structure using the graph cut method was adapted and an Ada-Boost classier was trained with features extracted from the segmentation to the 3D chest CT LNs. In [26,27], the authors used a new method based on integrating segmentation with a learning-based detector.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

Bnouni

Rekik

Rhim

et al. 2020

IJCDS

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Pelvic Lymph Nodes (PLNs) segmentation and classification are fundamental tools in the medical image analysis of pelvic gynecological cancer such as endometrial and cervical cancer. Often used by the radiologist, PLN classification requires detailed knowledge of the morphological features of PLNs, derived from size, shape, contour and heterogeneous appearance. Accurate PLN segmentation is an essential step in PLN classification. In order to supply the best assessment of a nodal status, semiautomatic and automatic PLN segmentation and classification methods are highly desired as they can strongly capture the wide variability in morphological features and reduce classification errors due to the inter and intra-observer variability, while avoiding the time-consuming for manual delineation of PLN boundary. Nevertheless, semi-automatic segmentation methods require the clinician intervention to select the initial seed point. However, typical semi-automatic PLN segmentation methods might fail due to (1) the intensity inhomogeneity, noise and low contrast in medical images, and (2) the position of the starting point. Thus, the performance of these methods can be enhanced by using a preprocessing-based iterative segmentation approach. Currently, Magnetic Resonance Imaging (MRI) is the most common imaging modality used for staging endometrial and cervical cancer, evaluation of PLN involvement and selection of therapeutic strategy. PLN detection using classic features can be challenging due to the similarity between normal and abnormal PLNs structures. In pelvic cancer and metastatic PLN, Diffusion Weighted (DW)-MRI exhibits brighter areas indicating tumor and metastatic PLN. This paper combines anatomic T2-Weighted (T2-w) imaging with DW imaging. Specifically, we propose a computer-aided pelvic framework, which leverages (1) an ensemble preprocessing method to improve PLN segmentation, (2) the iterative correction of the position of the initial point by executing the segmentation algorithm several times in succession, (3) the fusion of structural and diffusion MRI and, (4) the extraction of morphological features of segmented PLNs (axial T2-w image) as well as intensity feature derived from the fused image for the final classification of PLNs as suspect or non-suspect. Research in the field of PLN detection is important as it can help doctors to better detect cervical and endometrial cancer and decide the appropriate treatment. To the best of our knowledge, this is the first work to segment and classify PLN. Our preprocessing-based iterative segmentation approach significantly (p<0.05) improved comparison segmentation methods, with a segmentation accuracy boosted from 61.37% for the conventional region-growing algorithm to 66.53% for the proposed method. Furthermore, we obtained an average accuracy of 78.50% for pelvic nodule classification.

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Segmentation Based Features for Lymph Node Detection from 3-D Chest CT

Cited by 5 publications

References 12 publications

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

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