Automated Tumor Volumetry Using Computer-Aided Image Segmentation

Abstract

Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation.

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

“…Specifically, the geodesic distance between v i and v s was defined as min γ ∫ γ P(γ(s))ds, where γ is a path connecting v i to v s . Similar to the approach taken in [7], we set the weight P at each voxel to be proportional to its gradient magnitude, and the optimization was solved using the fast marching method [4,19]. Furthermore, the texture features describe the first and second order texture statistics computed from a gray-level co-occurrence matrix.…”

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

“…Its interaction tools include spherical approximation of abnormalities, coordinate definition, and region annotation that may be used as initialization of segmentation methods [18–20] and as region masks for further analysis (Fig. 2).…”

“…Representative applications of the interaction tools available in brain-CaPTk include two segmentation methods that require initialization, namely, GLISTRboost [18, 19] and geodesic distance transform (GDT) [20]. …”

“…The segmentation of brain structures based on the GDT [20], is a patient-based method fully incorporated in brain-CaPTk. In particular, a region is annotated to initialize the calculation of an “adaptive geodesic distance”, which at a given voxel i is a joint quantification of intensity variation and spatial distance between i and the seed region.…”

Spleen Segmentation and Assessment in CT Images for Traumatic Abdominal Injuries

“…Along with these techniques, there are several other schemes used for medical image segmentation for various applications such as bone, hemorrhage, and liver segmentation [4,10,12,18,23,25,26]. Most of these methods are some variant of techniques such as thresholding, region growing, and classification algorithms adapted to specific applications.…”