Evaluation of Lesion of Segmentation Methods in Peptide Receptor Radionuclide Therapy
Laaksonen, Oskari (2014)
2014
Signaalinkäsittelyn ja tietoliikennetekniikan koulutusohjelma
Tieto- ja sähkötekniikan tiedekunta - Faculty of Computing and Electrical Engineering
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Hyväksymispäivämäärä
2014-11-05
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201410291530
https://urn.fi/URN:NBN:fi:tty-201410291530
Tiivistelmä
Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are a group of tumours, which originate from the neuroendocrine system. GEP-NETs are characterized by overexpression of somatostatin receptors and can therefore be targeted using radiolabelled somatostatin analogues for peptide receptor radionuclide therapy (PRRT). The Lutetium-177 labelled somatostatin analogs DOTA-TOC and DOTA-TATE are being increasingly used for PRRT. The radioactive Lutetium-177 destroys tumor cells by emitting ionizing radiation. Unfortunately, also normal healthy organs express somatostatin receptors and thus the PRRT can cause significant radiation load to normal tissue. In order to protect the healthy organs and to maximize the radiation dose of the tumors radionuclide therapies need to be planned well by doing individual dosimetry.
Tumor dosimetry requires segmentation of the tumors from the background. Conventionally this segmentation has been performed manually, but the manual segmentation is often very dependent on skills of the operator who is doing the segmentation and it might not be very reproducible. These problems can be avoided with the use of automatic segmentation methods. Even though automatic segmentation has lately been a hot topic in positron emission tomography (PET) these methods have not been studied in PRRT.
In this Master of Science thesis automatic segmentation methods were studied from the PRRT perspective. Four segmentation methods were chosen to be evaluated: thresholding, k-means clustering, fuzzy-c-means clustering and expectation maximization. The evaluation was performed using simulated and real clinical single photon emission computed tomography (SPECT) images acquired during PRRT. The segmentation methods were compared with the help of Dice similarity coefficient (DSC), Classification error (CE) and the integral of the time activity curve.
The results state that expectation maximization is the most accurate algorithm of the four tested methods. It maximizes DSC and minimizes CE with every phantom. Thresholding gave promising results, but the optimal thresholding values had to be sought for each phantom, which made the method time-consuming. K-means clustering and fuzzy-c-means clustering were less successful. The accuracy of the methods with patient data is hard to estimate, due to the lack of the ground truth. However, the results with the patient data are very similar to the results obtained with the phantom data and they showed that segmentation has a big impact on the calculated tumor dose.
Tumor dosimetry requires segmentation of the tumors from the background. Conventionally this segmentation has been performed manually, but the manual segmentation is often very dependent on skills of the operator who is doing the segmentation and it might not be very reproducible. These problems can be avoided with the use of automatic segmentation methods. Even though automatic segmentation has lately been a hot topic in positron emission tomography (PET) these methods have not been studied in PRRT.
In this Master of Science thesis automatic segmentation methods were studied from the PRRT perspective. Four segmentation methods were chosen to be evaluated: thresholding, k-means clustering, fuzzy-c-means clustering and expectation maximization. The evaluation was performed using simulated and real clinical single photon emission computed tomography (SPECT) images acquired during PRRT. The segmentation methods were compared with the help of Dice similarity coefficient (DSC), Classification error (CE) and the integral of the time activity curve.
The results state that expectation maximization is the most accurate algorithm of the four tested methods. It maximizes DSC and minimizes CE with every phantom. Thresholding gave promising results, but the optimal thresholding values had to be sought for each phantom, which made the method time-consuming. K-means clustering and fuzzy-c-means clustering were less successful. The accuracy of the methods with patient data is hard to estimate, due to the lack of the ground truth. However, the results with the patient data are very similar to the results obtained with the phantom data and they showed that segmentation has a big impact on the calculated tumor dose.