Biomedical subjects

Jean-Marie Haefliger

Publications and source records attributed to Jean-Marie Haefliger.

2 recordsLinked to original sources

PubMed2002

A computer aided diagnostic system for radiotherapy planning.

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.

Humans↗

PubMed2002

A knowledge-based approach to automatic detection of the spinal cord in CT images.

Accurate planning of radiation therapy entails the definition of treatment volumes and a clear delimitation of normal tissue of which unnecessary exposure should be prevented. The spinal cord is a radiosensitive organ, which should be precisely identified because an overexposure to radiation may lead to undesired complications for the patient such as neuronal disfunction or paralysis. In this paper, a knowledge-based approach to identifying the spinal cord in computed tomography images of the thorax is presented. The approach relies on a knowledge-base which consists of a so-called anatomical structures map (ASM) and a task-oriented architecture called the plan solver. The ASM contains a frame-like knowledge representation of the macro-anatomy in the human thorax. The plan solver is responsible for determining the position, orientation and size of the structures of interest to radiation therapy. The plan solver relies on a number of image processing operators. Some are so-called atomic (e.g., thresholding and snakes) whereas others are composite. The whole system has been implemented on a standard PC. Experiments performed on the image material from 23 patients show that the approach results in a reliable recognition of the spinal cord (92% accuracy) and the spinal canal (85% accuracy). The lamina is more problematic to locate correctly (accuracy 72%). The position of the outer thorax is always determined correctly.

Adult↗