Towards automatic determination of total tumor burden from PET images

Steffen Renisch; Roland Opfer; Rafael Wiemker

doi:10.1117/12.844225

18 March 2010 Towards automatic determination of total tumor burden from PET images

Steffen Renisch, Roland Opfer, Rafael Wiemker

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 76241T (2010) https://doi.org/10.1117/12.844225
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Quantification of potentially cancerous lesions from imaging modalities, most prominently from CT or PET images, plays a crucial role both in diagnosing and staging of cancer as well as in the assessment of the response of a cancer to a therapy, e.g. for lymphoma or lung cancer. For PET imaging, several quantifications which might bear great discriminating potential (e.g. total tumor burden or total tumor glycolysis) involve the segmentation of the entirety of all of the cancerous lesions. However, this particular task of segmenting the entirety of all cancerous lesions might be very tedious if it has to be done manually, in particular if the disease is scattered or metastasized and thus consists of numerous foci; this is one of the reasons why only few clinical studies on those quantifications are available. In this work, we investigate a way to aid the easy determination of the entirety of cancerous lesions in a PET image of a human. The approach is designed to detect all hot spots within a PET image and rank their probability of being a cancerous lesion. The basis of this component is a modified watershed algorithm; the ranking is performed on a combination of several, primarily morphological measures derived from the individual basins. This component is embedded in a software suite to assess response to a therapy based on PET images. As a preprocessing step, potential lesions are segmented and indicated to the user, who can select the foci which constitute the tumor and discard the false positives. This procedure substantially simplifies the segmentation of the entire tumor burden of a patient. This approach of semi-automatic hot spot detection is evaluated on 17 clinical datasets.

Citation Download Citation

Steffen Renisch, Roland Opfer, and Rafael Wiemker "Towards automatic determination of total tumor burden from PET images", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241T (18 March 2010); https://doi.org/10.1117/12.844225

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