Cherry G. Ballangan

Lung tumor segmentation in PET images using graph cuts

The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill.

Automated Delineation of Lung Tumors in PET Images Based on Monotonicity and a Tumor-Customized Criterion

Reliable automated or semiautomated lung tumor delineation methods in positron emission tomography should provide accurate tumor boundary definition and separation of the lung tumor from surrounding tissue or “hot spots” that have similar intensities to the lung tumor. We propose a tumor-customized downhill (TCD) method to achieve these objectives. Our approach includes: 1) automatic formulation of a tumor-customized criterion to improve tumor boundary definition, 2) a monotonic property of the standardized uptake value (SUV) of tumors to separate the tumor from adjacent regions of increased metabolism (“hot spot”), and 3) accounts for tumor heterogeneity. Three simulated lesions and 30 PET-CT studies, grouped into “simple” and “complex” groups, were used for evaluation. Our main findings are that TCD, when compared to the threshold based on 40% and 50% maximum SUV, adaptive threshold, Fuzzy c-means, and watershed techniques achieved the highest Dices similarity coefficient average for simulation data (0.73) and “complex” group (0.71); the least volumetric error in the “simple” (1.76 mL) and the “complex” group (14.59 mL); and TCD solves the problem of leakage into adjacent tissues when many other techniques fail.

Lung Tumor Delineation Based on Novel Tumor-Background Likelihood Models in PET-CT Images

Accurate parenchymal lung tumor delineation with PET-CT can be problematic given the inherent tumor heterogeneity and proximity / involvement of extra-parenchymal tissue. In this paper, we propose a tumor delineation approach that is based on new tumor-background likelihood models in PET and CT. By incorporating the intensity downhill feature in PET as a distance cost into the background likelihood function of CT, our delineation method avoids leakage to structures with similar intensities on PET and CT, but at the same time follows the boundary definition in CT when it is distinct. We validated our method on 40 NSCLC patient datasets with manual delineation by three clinical experts. Our method achieved an average Dices similarity coefficient (DSC) of 0.80 ±0.08 in the simple group, and 0.77 ±0.06 in the complex group. The t-test demonstrated that our method statistically outperformed the four other methods. Our method was able to delineate complex tumors that were located in close proximity to other structures with similar intensities.

Automated detection and delineation of lung tumors in PET-CT volumes using a lung atlas and iterative mean-SUV threshold

Automated segmentation for the delineation of lung tumors with PET-CT is a challenging task. In PET images, primary lung tumors can have varying degrees of tracer uptake, which sometimes does not differ markedly from normal adjacent structures such as the mediastinum, heart and liver. In addition, separation of tumor from adjacent soft tissues and bone in the chest wall is problematic due to limited resolution. For CT, the tumor soft tissue density can be similar to that in the blood vessels and the chest wall; and although CT provides better boundary definition, exact tumor delineation is also difficult when the tumor density is similar to adjacent structures. We propose an innovative automated adaptive method to delineate lung tumors in PET-CT images in conjunction with a lung atlas in which an iterative mean-SUV (Standardized Uptake Value) threshold is used to gradually define the tumor region in PET. Tumor delineation in the CT data is performed using region growing and seeds obtained autonomously from the PET tumor regions. We evaluated our approach in 13 patients with non-small cell lung cancer (NSCLC) and found it could delineate tumors of different size, shape and location, even when when the NSCLC involved the chest wall.

Lung segmentation and tumor detection from CT thorax volumes of FDG PET-CT scans by template registration and incorporation of functional information

Automatic segmentation and detection of lungs and tumors in FDG PET-CT images is potentially beneficial in the diagnosis and staging of patients with non-small cell lung cancer (NSCLC). However, simultaneous lung segmentation and tumor detection is not a trivial task, particularly due to noise in the datasets, proximity of the lung lesion to the mediastinum and chest wall in certain instances, and disease involvement of non-enlarged lymph nodes.

Lung tumor delineation in PET-CT images based on a new segmentation energy

Accurate lung tumor delineation from positron emission tomography (PET) - computed tomography (CT) images is important for patient management. However, this task is challenging, especially when a tumor abuts or involves the chest wall or mediastinum; or when it is located in close proximity to the heart or liver. Combining PET and CT can improve the accuracy of tumor delineation, but current methods might be prone to leakage or require that the tumor already be isolated within a box.

Lung tumor delineation in PET-CT images using a downhill region growing and a Gaussian mixture model

Combined PET-CT is now increasingly used for the clinical evaluation of cancer and is arguably the best tool to stage non-small cell lung cancer (NSCLC). We propose a framework to better delineate lung tumors which utilizes information from PET and CT images. The framework is based on a downhill region growing technique for PET and a Gaussian mixture model for CT images. We applied our framework in 20 PET-CT studies from patients with NSCLC. Experiments show that our method is able to delineate lung tumors in complex cases where the tumors are located near other organs with similar intensities in PET images or when the tumors extends into the chest wall or the mediastinum. We also compared 10 of the datasets with experts performing manual delineation, which produced a volumetric overlapped fraction of 0.78 ± 0.10.

Elastic & Efficient Three-Dimensional Registration for Abdominal Images

Biomedical image registration plays an important role in the intelligent use of large amount of medical data to establish safe healthcare. One of main challenges in abdominal image registration is to deal with elastic deformations caused by the shape and volume changes of internal organs, as well as heartbeat and breath. Other challenges include high computational efficiency required for clinical applications and accurate registration for 3D multimodality images. In this paper, an elastic, efficient and automatic 3D abdominal image registration method is proposed. Our proposed algorithm is divided into two stages and applied to register slice pairs of 3D volume data. First, non-iterative affine registration is performed to fix the global deformation by minimizing mean squared error (MSE). Then, effective active contour-based elastic registration is performed to correct the local deformations. This method has been validated by the experiments on both 3D CT-CT registration and CT-PET registration

Lung tumor segmentation and separation from PET volumes based on Tumor-Customized Downhill

Positron emission tomography (PET) plays an essential role in lung cancer diagnosis, staging, and treatment. However, it is difficult to accurately segment and separate tumors residing in close proximity. It is even more challenging for tumor segmentation from PET due to its heterogeneous density distribution and the difficulty in finding the stopping criterion for delineation. To address these issues, in this paper, we investigated the tumor segmentation and separation by using Tumor-Customized Downhill (TCD) method and compared TCD with other widely used methods including 40% and 50% of maximum SUV, and watershed technique. Our quantitative and qualitative comparison and validation on seven clinical studies, including thirteen tumors demonstrated that TCD outperformed its counterpart methods in terms of tumor segmentation and separation.

The impact of reconstruction algorithms on semi-automatic small lesion segmentation for PET: A phantom study

A robust lesion segmentation method is critical for quantification of lesion activity in positron emission tomography (PET), especially for the cases where lesion boundary is not discernible in the corresponding computed tomography (CT). However, lesion delineation in PET is a challenging task, especially for small lesions, due to the low intrinsic resolution, image noise and partial volume effect. The combinations of different reconstruction methods and post-reconstruction smoothing on PET images also affect the segmentation result significantly which has always been overlooked. Therefore, the aim of this study was to investigate the impact of different reconstruction methods on semi-automated small lesion segmentation for PET images. Four conventional segmentation methods were evaluated including region growing technique based on maximum intensity (RGmax) and mean intensity (RGmean) thresholds, Fuzzy c-mean (FCM) and watershed (WS) technique. All these methods were evaluated on a physical phantom scan which was reconstructed with Ordered Subset Expectation Maximization (OSEM) with Gaussian post-smoothing and Maximum a Posteriori (MAP) with quadratic prior respectively. The results demonstrate that: 1) the performance of all the segmentation methods subject to the smoothness constraint applied on the reconstructed images; 2) FCM method applied on MAP reconstructed images yielded overall superior performance than other evaluated combinations.