PubMed Health⌕ Search

PubMed · 12575438

A computer aided diagnostic system for radiotherapy planning.

Abstract

Planning for radiation therapy intervention implies the definition of treatment volumes as well as a clear delimitation of normal tissue. This paper presents a Computer Aided Diagnostic system for the automatic CT image analysis. Two important problems are solved: the spinal cord segmentation and the detection of lung metastases. Some subordinate problems are also solved: the detection of spinal canal, lamina, lungs, and ribs, as well as the identification of thorax contour. The developed methodologies use a knowledge-driven image processing based on Anatomical Structures Maps and task-oriented architecture. Experiments were performed on CT images from La Chaux de Fonds Hospital (Switzerland). Evaluations were performed using a visual inspection of the contours projected on the CT image slices. The radiologist decided whether each of the contours obtained with our system was acceptable or not. The accuracy of the method was defined as the fraction of CT slices in which the particular contour was correctly located. In the case of spinal cord segmentation, the procedure was tested on 23 patients (1051 images), resulting in an accuracy of 91%. In the case of lung tumors detection, the method showed an accuracy of > 90%, with testing performed on 20 patients for a total of 988 images. The experiments performed show that the method is reliable, with possible future application in an oncology department.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Neculai Archip, Pierre-Jean Erard, Jean-Marie Haefliger, Jean-François Germond. 2002. A computer aided diagnostic system for radiotherapy planning.. https://doi.org/10.1016/s0939-3889(15)70480-0

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗