Biomedical subjects

Binsheng Zhao

Publications and source records attributed to Binsheng Zhao.

4 recordsLinked to original sources

PubMed2005

Pulmonary metastases: effect of CT section thickness on measurement--initial experience.

PURPOSE: To assess the effect of commonly used computed tomographic (CT) section thicknesses on metastatic tumor measurements calculated with unidimensional, bidimensional, area, and volumetric methods. MATERIALS AND METHODS: Analysis and data collection were approved by the Institutional Review Board, with waived informed patient consent. Forty-two pulmonary metastases in 10 patients (three men and seven women; age range, 43-83 years; mean age, 65.4 years) were analyzed on CT scans obtained with 3.75-, 5.0-, and 7.5-mm section thicknesses. The lesions were automatically delineated by using a three-dimensional multicriteria segmentation algorithm. Unidimensional (the largest diameter), bidimensional (the product of the two maximal perpendicular diameters), maximal cross-sectional area, and volumetric measurements were automatically obtained for each pulmonary lesion on each section thickness. Means and variances were calculated, and the differences across the three section thicknesses for each of the four measurements were studied by using linear mixed-effects models. The Levene test was used to study the equality of variances. RESULTS: Differences in the means for unidimensional, bidimensional, and area measurements were significant between a section thickness of 3.75 and 5.0 mm (unidimensional, P=.05; bidimensional, P=.05; area, P=.01) and 3.75 and 7.5 mm (unidimensional, P=.06; bidimensional, P=.03; area, P=.02), but not 5.0 and 7.5 mm. There was a significant difference in volumetric measurement as section thickness decreased from 7.5 to 5.0 mm (P <.001) and from 7.5 to 3.75 mm (P <.001). Although there was a slight trend for differences in the variances across section thickness for each measurement, none of the differences were significant. CONCLUSION: Volumetric tumor measurements change with a reduction in section thickness from 7.5 to 5.0 and 3.75 mm. For unidimensional measurement, no change was found when thickness decreased from 7.5 to 5.0 mm.

Adult↗

PubMed2003

Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm.

Increasingly, computed tomography (CT) offers higher resolution and faster acquisition times. This has resulted in the opportunity to detect small lung nodules, which may represent lung cancers at earlier and potentially more curable stages. However, in the current clinical practice, hundreds of such thin-sectional CT images are generated for each patient and are evaluated by a radiologist in the traditional sense of looking at each image in the axial mode. This results in the potential to miss small nodules and thus potentially miss a cancer. In this paper, we present a computerized method for automated identification of small lung nodules on multislice CT (MSCT) images. The method consists of three steps: (i) separation of the lungs from the other anatomic structures, (ii) detection of nodule candidates in the extracted lungs, and (iii) reduction of false-positives among the detected nodule candidates. A three-dimensional lung mask can be extracted by analyzing density histogram of volumetric chest images followed by a morphological operation. Higher density structures including nodules scattered throughout the lungs can be identified by using a local density maximum algorithm. Information about nodules such as size and compact shape are then incorporated into the algorithm to reduce the detected nodule candidates which are not likely to be nodules. The method was applied to the detection of computer simulated small lung nodules (2 to 7 mm in diameter) and achieved a sensitivity of 84.2% with, on average, five false-positive results per scan. The preliminary results demonstrate the potential of this technique for assisting the detection of small nodules from chest MSCT images.

Algorithms↗

PubMedDate not supplied

Lymph node segmentation from CT images using fast marching method.

Accurate lymph node size analysis is important medically. This paper presents an improved fast marching method to perform semi-automatic segmentation for lymph node from CT images. In this work, we have incorporated the gray scale information of the target region into the fast marching speed term and have given a hard constraint for the stop criteria, instead of only using the spatial image gradient, to remedy the 'boundary leaking' problem of the traditional fast marching method. Various experimental results are provided to demonstrate the effectiveness of the proposed method.

Humans↗

PubMedDate not supplied

Characterization of small nodules by automatic segmentation of X-ray computed tomography images.

OBJECTIVE: To characterize the ability of an automatic lung nodule segmentation algorithm to measure small nodule dimensions and growth rates. METHODS: A phantom of 20 sets of 6 balls each (11 different nylon balls and 9 acrylic balls) of 1 to 9.5 mm in diameter, in foam, was imaged using x-ray computed tomography with slice thicknesses of 5, 2.5, and 1.25 mm, pitches of 3 and 6, and standard and lung resolution. Measurements consisted of volume and maximum in-plane cross-sectional areas and their derived maximum and effective diameters. Growth rates were simulated using pairs of groups of balls. RESULTS: Volume measurements overestimate volume, more so for thicker slices. For the largest balls, the error is 60% for 5-mm slices and 20% for 1.25-mm slices. Effective diameter calculated from volume better approximates actual diameter. For area measurements, errors are 0% to 5% for the largest balls, and the effective and actual diameters are closely matched. CONCLUSIONS: Below 5 mm in diameter, changes in volume should reach 100% for reliable indication of growth. Above 6 mm, the threshold for detecting change is on the order of 25% growth. Even under ideal conditions, results indicate the need for caution when making a diagnosis of malignancy on the basis of volume change.

Algorithms↗