RESUMO
We proposed a statistical modeling method for the quantitative evaluation of segmentation methods used in image guided radiotherapy. A statistical model parameterized on a Beta distribution was built upon the observations of the volume overlap between the segmented structure and the referenced structure. A statistical performance profile (SPP) was then estimated from the model using the generalized maximum likelihood approach. The SPP defines the probability density function characterizing the distribution of performance values and provides a graphical visualization of the segmentation performance. Different segmentation approaches may be influenced by image quality or observer variability. Our statistical model was able to quantify the impact of these variations and displays the underlying statistical performance of the segmentation algorithm. We demonstrated the efficacy of this statistical model using both simulated data and clinical evaluation studies in head and neck radiotherapy. Furthermore, the resulting SPP facilitates the measurement of the correlation between quantitative metrics and clinical experts' decision, and ultimately is able to guide the clinicians in selecting segmentation methods for radiotherapy.
Assuntos
Neoplasias de Cabeça e Pescoço/diagnóstico por imagem , Neoplasias de Cabeça e Pescoço/radioterapia , Reconhecimento Automatizado de Padrão/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radioterapia Conformacional/métodos , Radioterapia Guiada por Imagem/métodos , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Interpretação Estatística de Dados , Humanos , Imageamento Tridimensional/métodos , Intensificação de Imagem Radiográfica/métodos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
This paper introduces a novel method to score how well proposed fused image quality measures (FIQMs) indicate the effectiveness of humans to detect targets in fused imagery. The human detection performance is measured via human perception experiments. A good FIQM should relate to perception results in a monotonic fashion. The method computes a new diffuse prior monotonic likelihood ratio (DPMLR) to facilitate the comparison of the H(1) hypothesis that the intrinsic human detection performance is related to the FIQM via a monotonic function against the null hypothesis that the detection and image quality relationship is random. The paper discusses many interesting properties of the DPMLR and demonstrates the effectiveness of the DPMLR test via Monte Carlo simulations. Finally, the DPMLR is used to score FIQMs with test cases considering over 35 scenes and various image fusion algorithms.