The assessment of pituitary tumor (PT) volume is important in the treatment and follow-up of patients with PT. Previously, PT volume estimation has been performed by conventional geometric equations (CGE) such as (simplified ellipsoid volume equation) and (sphere), both presuming a symmetric tumor shape, which occurs uncommonly in patients with PT. In contrast, three-dimensional (3D) voxel-based software segmentation takes the irregular and asymmetric shapes that PTs often possess into account and might be a more accurate method for PT volume segmentation. The purpose of this study is twofold. (1) To compare 3D segmentation with CGE for PT volume estimation. (2) To assess inter-rater reliability in 3D segmentation of PTs.  Nineteen high-resolution (1mm slice thickness) T1-weighted MRI examinations of patients with PT were independently analyzed and manually segmented, using the software ITK-SNAP, by two certified neuroradiologists. Concurrently, the volumes of the PTs were estimated with and by a clinician, and the results were compared with the corresponding segmented volumes.  There was a significant decrease in PT volume attained from the segmentations compared with the calculations made with (  < 0.001, mean volume 18% higher than segmentation) and (  < 0.001, mean volume 28% higher than segmentation). The intraclass correlation coefficient (ICC) for the two sets of segmented PTs was 0.99.  CGE ( and ) significantly overestimates PT volume compared with 3D volumetric segmentation. The inter-rater agreement on manual 3D volumetric software segmentation is excellent.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6133660PMC
http://dx.doi.org/10.1055/s-0037-1618577DOI Listing

Publication Analysis

Top Keywords

conventional geometric
8
geometric equations
8
volume estimation
8
volume
6
segmentation
5
three-dimensional volumetric
4
volumetric segmentation
4
segmentation pituitary
4
pituitary tumors
4
tumors assessment
4

Similar Publications

Geometry-Based Synchrosqueezing S-Transform with Shifted Instantaneous Frequency Estimator Applied to Gearbox Fault Diagnosis.

Sensors (Basel)

January 2025

Heime (Tianjin) Electrical Engineering Systems Co., Ltd., Tianjin 301700, China.

This paper introduces a novel geometry-based synchrosqueezing S-transform (GSSST) for advanced gearbox fault diagnosis, designed to enhance diagnostic precision in both planetary and parallel gearboxes. Traditional time-frequency analysis (TFA) methods, such as the Synchrosqueezing S-transform (SSST), often face challenges in accurately representing fault-related features when significant mode closely spaced components are present. The proposed GSSST method overcomes these limitations by implementing an intuitive geometric reassignment framework, which reassigns time-frequency (TF) coefficients to maximize energy concentration, thereby allowing fault components to be distinctly isolated even under challenging conditions.

View Article and Find Full Text PDF

Design and Study of a Novel P-Type Junctionless FET for High Performance of CMOS Inverter.

Micromachines (Basel)

January 2025

State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China.

In this paper, a novel p-type junctionless field effect transistor (PJLFET) based on a partially depleted silicon-on-insulator (PD-SOI) is proposed and investigated. The novel PJLFET integrates a buried N+-doped layer under the channel to enable the device to be turned off, leading to a special work mechanism and optimized performance. Simulation results show that the proposed PJLFET demonstrates an I/I ratio of more than seven orders of magnitude, with I reaching up to 2.

View Article and Find Full Text PDF

Representational geometry explains puzzling error distributions in behavioral tasks.

Proc Natl Acad Sci U S A

January 2025

Department of Economics, Columbia University, New York, NY 10027.

Measuring and interpreting errors in behavioral tasks is critical for understanding cognition. Conventional wisdom assumes that encoding/decoding errors for continuous variables in behavioral tasks should naturally have Gaussian distributions, so that deviations from normality in the empirical data indicate the presence of more complex sources of noise. This line of reasoning has been central for prior research on working memory.

View Article and Find Full Text PDF

Fitting Geometric Shapes to Fuzzy Point Cloud Data.

J Imaging

January 2025

Faculty of Information Technology and Communication Sciences, Mathematics Research Centre, Tampere University, Korkeakoulunkatu 1, 33720 Tampere, Finland.

This article describes procedures and thoughts regarding the reconstruction of geometry-given data and its uncertainty. The data are considered as a continuous fuzzy point cloud, instead of a discrete point cloud. Shape fitting is commonly performed by minimizing the discrete Euclidean distance; however, we propose the novel approach of using the expected Mahalanobis distance.

View Article and Find Full Text PDF

The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!