Reto Merges

On simulating subjective evaluation using combined objective metrics for validation of 3D tumor segmentation

In this paper, we present a new segmentation evaluation method that can simulate radiologists subjective assessment of 3D tumor segmentation in CT images. The method uses a new metric defined as a linear combination of a set of commonly used objective metrics. The weighing parameters of the linear combination are determined by maximizing the rank correlation between radiologists subjective rating and objective measurements. Experimental results on 93 lesions demonstrate that the new composite metric shows better performance in segmentation evaluation than each individual objective metric. Also, segmentation rating using the composite metric compares well with radiologists subjective evaluation. Our method has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale segmentation evaluation studies.

Spectrum of Functioning Islet Cell Tumor on Multislice Computed Tomography: Experience on 70 Patients

OBJECTIVE To review experience in preoperative detection of islet cell tumors using multislice computed tomography (MSCT) and summarize various imaging features of functioning islet cell tumors on enhanced MSCT. METHODS Seventy patients with clinical or pathological diagnosis of functioning pancreatic islet cell tumor between October 2003 and February 2007 were included in this retrospective study. Seventy-four enhanced MSCT scans in these patients were identified. All MSCT scans were interpreted by two experienced radiologists by consensus interpretation. Surgery and pathology reports were used to confirm the diagnosis, localization, and size of tumors. RESULTS Totally, 73 functioning islet cell tumors including 65 benign insulinomas, 2 benign glucagonomas, 3 malignant insulinomas, and 3 malignant glucagonomas were pathologically diagnosed. Tumors in only two cases were not found by MSCT. In 67 benign lesions, 32 showed typical enhancement style, 21 showed prolonged enhancement in portal venous phase, 4 showed delayed enhancement, 4 had iso-dense enhancement with normal pancreatic parenchyma, 2 had no enhancement at all in arterial phase and portal venous phase, and 4 had inhomogeneous enhancement with necrosis or cyst-formation. Patchy or spotty calcifications were found in 3 of the 67 tumors. In 6 malignant islet cell tumors, vessel invasion (2/6) and bowel invasion (1/6) were seen. Different enhancement patterns were shown. All hepatic metastases showed hyper-enhancement during their arterial phase. CONCLUSIONS Pancreatic islet cell tumor may display a wide spectrum of presentations in MSCT. Tumors with unusual appearances often present as diagnostic challenges. Non-contrast and post-contrast multiphase scans are recommended for the localization of functioning islet cell tumors.

Anatomical-based segmentation with stenosis bridging and gap closing in atherosclerotic cardiac MSCT

In the diagnosis of coronary artery disease, 3D-multi-slice computed tomography (MSCT) has recently become more and more important. In this work, an anatomical-based method for the segmentation of atherosclerotic coronary arteries in MSCT is presented. This technique is able to bridge severe stenosis, image artifacts or even full vessel occlusions. Different anatomical structures (aorta, blood-pool of the heart chambers, coronary arteries and their orifices) are detected successively to incorporate anatomical knowledge into the algorithm. The coronary arteries are segmented by a simulated wave propagation method to be able to extract anatomically spatial relations from the result. In order to bridge segmentation breaks caused by stenosis or image artifacts, the spatial location, its anatomical relation and vessel curvature-propagation are taken into account to span a dynamic search space for vessel bridging and gap closing. This allows the prevention of vessel misidentifications and improves segmentation results significantly. The robustness of this method is proven on representative medical data sets.

Comprehensive Evaluation of an Image Segmentation Technique for Measuring Tumor Volume from CT Images

Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation. In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200 tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can provide complementary information regarding the quality of the segmentation results. The reproducibility was measured by the variation of the volume measurements from 10 independent segmentations. The effect of disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale evaluation of segmentation techniques for other clinical applications.