&lt;title&gt;Local-cost computation for efficient segmentation of 3D objects with live wire&lt;/title&gt;

Schenk, Andrea; Prause, Guido P. M.; Peitgen, Heinz‐Otto

doi:10.1117/12.431015

Cited by 35 publications

(18 citation statements)

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“…A comprehensive review of CT-based liver segmentation techniques was done by Campadelli et al, 32 describing additional methods that employed live wire, [33][34][35][36] gray-level analysis, 32,[37][38][39][40][41][42][43][44] neural networks, [45][46][47][48] model fitting, [49][50][51] level sets, 52 and probabilistic atlases. [53][54][55][56] Campadelli et al highlighted respective advantages and drawbacks that limit the use of such techniques in the clinic.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

et al. 2010

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Purpose:To investigate the potential of the normalized probabilistic atlases and computer-aided medical image analysis to automatically segment and quantify livers and spleens for extracting imaging biomarkers ͑volume and height͒. Methods: A clinical tool was developed to segment livers and spleen from 257 abdominal contrastenhanced CT studies. There were 51 normal livers, 44 normal spleens, 128 splenomegaly, 59 hepatomegaly, and 23 partial hepatectomy cases. 20 more contrast-enhanced CT scans from a public site with manual segmentations of mainly pathological livers were used to test the method. Data were acquired on a variety of scanners from different manufacturers and at varying resolution. Probabilistic atlases of livers and spleens were created using manually segmented data from ten noncontrast CT scans ͑five male and five female͒. The organ locations were modeled in the physical space and normalized to the position of an anatomical landmark, the xiphoid. The construction and exploitation of liver and spleen atlases enabled the automated quantifications of liver/spleen volumes and heights ͑midhepatic liver height and cephalocaudal spleen height͒ from abdominal CT data. The quantification was improved incrementally by a geodesic active contour, patient specific contrast-enhancement characteristics passed to an adaptive convolution, and correction for shape and location errors. Results: The livers and spleens were robustly segmented from normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 96.2%/92.7%, the volume/height errors were 2.2%/2.8%, the root-mean-squared error ͑RMSE͒ was 2.3 mm, and the average surface distance ͑ASD͒ was 1.2 mm. The spleen quantification led to 95.2%/91% Dice/Tanimoto overlaps, 3.3%/ 1.7% volume/height errors, 1.1 mm RMSE, and 0.7 ASD. The correlations ͑R 2 ͒ with clinical/ manual height measurements were 0.97 and 0.93 for the spleen and liver, respectively ͑p Ͻ 0.0001͒. No significant difference ͑p Ͼ 0.2͒ was found comparing interobserver and automaticmanual volume/height errors for liver and spleen. Conclusions: The algorithm is robust to segmenting normal and enlarged spleens and livers, and in the presence of tumors and large morphological changes due to partial hepatectomy. Imaging biomarkers of the liver and spleen from automated computer-assisted tools have the potential to assist the diagnosis of abdominal disorders from routine analysis of clinical data and guide clinical management.

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Section: Introductionmentioning

confidence: 99%

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

et al. 2010

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“…Many variants of this technique have been developed for years. In (Schenk et al, 2000;Schenk et al, 2001) the authors modified the original algorithm to reduce its computation time in order to extend it to 3D images. These methods speed up the work of radiologists manually drawing liver boundaries, but are highly dependent on the operator's skill.…”

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confidence: 99%

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Fernandez-de-Manuel¹,

Ledesma-Carbayo²,

Jiménez-Carretero³

et al. 2011

Theory and Applications of CT Imaging and Analysis

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“…Up to now the segmentation happens manually or with a semi-automatic computer software. The semiautomatic segmentation methods, like live wire segmentation [14] or ziplock snakes [15], require a high degree of interaction. Therefore, we wanted to find a 3D segmentation method with a small part of interaction.…”

Section: Test Of the Evaluation Strategymentioning

confidence: 99%

Three-level evaluation process for segmentation methods in medical imaging

Pohle

Toennies

2002

SPIE Proceedings

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We propose an evaluation process for segmentation which is made up of three different levels. It enables us to carry out the time consuming steps only for those segmentation methods for which a successful segmentation is foreseeable. In the first level the developer of a segmentation method does a coarse analysis of the usefulness of the individual segmentation methods by means of visual assessment of the results for few image examples. Methods which have been judged useful at the first level are investigated in a second evaluation step as to the stability of the segmentation results in case of slight deviations in the images. For the reproduction of the image formation process a multitude of realizations of a given region of interest are produced by means of the bootstrap technique. At the third level of the evaluation process the segmentation methods are tested for segmentation errors. The segmentation methods are judged by means of empirical discrepancy values, and the effectiveness of a method chosen for the respective task is finally estimated.Keywords: Evaluation, 3D segmentation, semi-automatic segmentation, image foresting transformation, hybrid rendering, bootstrap 1.IntroductionSegmentation is an essential step in the automatic analysis of medical images. Most segmentation methods contain a model which is derived from knowledge about the problem, from sample images and from information about the segments to be extracted. The current segmentation methods in the field of medical image processing use various models to characterize segments. It may consist of knowledge about models from the image formation process, of topological and of geometric hypotheses. It is the goal of the evaluation process to examine whether the model information is suitable and sufficient for the description of the reality. Due to the high variability among the medical images, the use of segmentation algorithms for a limited number of example images alone delivers no precise assessment of the general quality of a given method. An investigation of a large number of data sets delivers of course a higher safety for the estimate of the performance of an algorithm, but it is time-consuming. Standardized methods for the evaluation of segmentation methods do not exist yet [1], [2]. Therefore we propose in this paper an evaluation process made up of three different levels, which enables us to carry out the time consuming steps only for those processes for which a successful segmentation is foreseeable. This procedure allows to integrate the evaluation process in the whole stage of development of a new segmentation method. 2.Evaluation processOur evaluation process consists of three steps which are carried out one after the other (see Figure 1). We consider the segmentation as applying model information to the image. When the developer starts to develop a segmentation method for a new task, often only few examples images are available. Based on these images the developer starts the first step of the evaluation process. In this ste...

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<title>Local-cost computation for efficient segmentation of 3D objects with live wire</title>

Cited by 35 publications

References 0 publications

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Three-level evaluation process for segmentation methods in medical imaging

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