Automatic segmentation of anatomical structures in medical images is a valuable tool for efficient computer-aided radiotherapy and surgery planning and an enabling technology for dynamic adaptive radiotherapy. This paper presents the design, algorithms and validation of new software for the automatic segmentation of CT images used for radiotherapy treatment planning. A coarse to fine approach is followed that consists of presegmentation, anatomic orientation and structure segmentation. No user input or a priori information about the image content is required. In presegmentation, the body outline, the bones and lung equivalent tissue are detected. Anatomic orientation recognizes the patient's position, orientation and gender and creates an elastic mapping of the slice positions to a reference scale. Structure segmentation is divided into localization, outlining and refinement, performed by procedures with implicit anatomic knowledge using standard image processing operations. The presented version of algorithms automatically segments the body outline and bones in any gender and patient position, the prostate, bladder and femoral heads for male pelvis in supine position, and the spinal canal, lungs, heart and trachea in supine position. The software was developed and tested on a collection of over 600 clinical radiotherapy planning CT stacks. In a qualitative validation on this test collection, anatomic orientation correctly detected gender, patient position and body region in 98% of the cases, a correct mapping was produced for 89% of thorax and 94% of pelvis cases. The average processing time for the entire segmentation of a CT stack was less than 1 min on a standard personal computer. Two independent retrospective studies were carried out for clinical validation. Study I was performed on 66 cases (30 pelvis, 36 thorax) with dosimetrists, study II on 52 cases (39 pelvis, 13 thorax) with radio-oncologists as experts. The experts rated the automatically produced structures on the scale 1-excellent (no corrections necessary, maximum time saving), 2-good (corrections necessary for up to 1/3 of slices), 3-acceptable (major corrections necessary, but still time saving), 4-not acceptable (manual redrawing more efficient, no time saving). A rating

Download full-text PDF

Source
http://dx.doi.org/10.1088/0031-9155/53/6/017DOI Listing

Publication Analysis

Top Keywords

automatic segmentation
12
anatomic orientation
12
time saving
12
images radiotherapy
8
radiotherapy planning
8
implicit anatomic
8
anatomic knowledge
8
structure segmentation
8
body outline
8
outline bones
8

Similar Publications

Serving as a dedicated process analytical technology (PAT) tool for biomass monitoring and control, the capacitance probe, or dielectric spectroscopy, is showing great potential in robust pharmaceutical manufacturing, especially with the growing interest in integrated continuous bioprocessing. Despite its potential, challenges still exist in terms of its accuracy and applicability, particularly when it is used to monitor cells during stationary and decline phases. In this study, data pre-processing methods were first evaluated through cross-validation, where the first-order derivative emerged as the most effective method to diminish variability in prediction accuracy across different training datasets.

View Article and Find Full Text PDF

Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm.

Sci Rep

January 2025

Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamilnadu, India, 641010.

The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals.

View Article and Find Full Text PDF

COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database.

Sci Data

January 2025

Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database.

View Article and Find Full Text PDF

End-to-End CT Radiomics-Based Pipeline for Predicting Renal Interstitial Fibrosis Grade in CKD Patients.

Acad Radiol

January 2025

Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310007, Zhejiang Province, China (Y.R., W.L., Y.Z., S.K., F.C.). Electronic address:

Rationale And Objectives: Non-invasive assessment of renal fibrosis in patients with chronic kidney disease (CKD) remains a clinical challenge. This study aims to integrate radiomics and clinical factors to develop an end-to-end pipeline for predicting interstitial fibrosis (IF) in CKD patients.

Materials And Methods: This retrospective study included 80 patients with CKD, with 53 patients in training set and 27 patients in test set.

View Article and Find Full Text PDF

Objective: The extent of resection (EOR) and postoperative residual tumor (RT) volume are prognostic factors in glioblastoma. Calculations of EOR and RT rely on accurate tumor segmentations. Raidionics is an open-access software that enables automatic segmentation of preoperative and early postoperative glioblastoma using pretrained deep learning models.

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!