In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.
Both systems have a very good ability to create highly conformal volumetric dose distributions. Median HI of PTV for IMRT and CK plans were 1.08 and 1.33, respectively (p < 0.001).
IMRT significantly improved conformity and homogeneity index for plans. Heart and lung volumes receiving high doses were decreased, but OAR receiving low doses was increased.
Abstract. In this paper, we re-examine the cellular automata(CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.
The effectiveness of PET-CT (positron emission tomographyÀcomputed tomography) was investigated for staging target delineation compared with CT-MR (computed tomographyÀmagnetic resonance) and early response of intensity-modulated radiotherapy (IMRT). Gross tumour volumeÀclinical target volume (GTV-CTV) differences between PET-CT and CT-MR for 14 nasopharyngeal carcinoma (NPC) patients were compared. Evaluation of doses of organs at risk (OARs) was done by IMRT plans. Responses of IMRT were evaluated with both sets. PET-CT changed MR-based TNM (Tumour Lymph Nodes Metastasis) in 11 of 14 patients. The median GTVNP (nasopharyx gross tumour volume) was 49.25 and 18.8 cm 3 for CT-MR and PET-CT, respectively. In eight cases, GTVNP in the PET-CT was smaller than the CT-MR. The PET-CT presented a larger GTVNP than the CT-MR for six cases. Mean doses for the parotid glands were found to be higher than in CT-MR-based plan in one patient although he had smaller GTVNP at the PET-CT. The median follow-up was 16 months. Only one patient experienced recurrence in the CTVNP (nasopharyx clinical target volume). MR showed a decrease in the size-number of lymph nodes in four patients whereas PET-CT showed no uptake. All patients had positive responses to IMRT in their second control MR and PET-CT. PET-CT could improve tumour delineation. This enables an increase in dose inside the CTV. PET-CT provided significant information on the control scans for most of our patients whose MR imaging showed residual or recurrence.
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