Maria Jimena Costa

A Retrieval-Based Computer-Aided Diagnosis System for the Characterization of Liver Lesions in CT Scans

RATIONALE AND OBJECTIVES To evaluate a computer-aided diagnosis (CADx) system for the characterization of liver lesions in computed tomography (CT) scans. The stand-alone predictive performance of the CADx system was assessed and compared to that of three radiologists who were provided with the same amount of image information to which the CADx system had access. MATERIALS AND METHODS The CADx system operates as an image search engine exploiting texture analysis of liver lesion image data for the lesion in question and lesions from a database. A region of interest drawn around an indeterminate liver lesion is used as input query. The CADx system retrieves lesions of similar histology (benign/malignant), density (hypodense/hyperdense), or type (cyst/hemangioma/metastasis). The systems performance was evaluated with leave-one-patient-out receiver operating characteristic area under the curve on 685 CT scans from 372 patients that contained 2325 liver lesions (193 <1 cm(3)). Sensitivity, specificity, and positive and negative predictive values were evaluated separately for subcentimeter lesions. Results were compared to those of three radiologists who rated 83 liver lesions (20 hemangiomas, 20 metastases, 20 cysts, 20 hepatocellular carcinomas, and 3 focal nodular hyperplasias) displaying only the liver. RESULTS The CADx systems leave-one-patient-out receiver operating characteristic area under the curve was 97.1% for density, 91.4% for histology, and 95.5% for lesion type. For subcentimeter lesions, input of additional semantic information improved the systems performance. The CADx system has been proved to significantly outperform radiologists in discriminating lesion histology and type, provided the radiologists have no access to information other than the image. The radiologists were most reliable in diagnosing hemangioma given the limited image data. CONCLUSIONS The CADx system under study discriminated reliably between various liver lesions, even outperforming radiologists when accessing the same image information and demonstrated promising performance in classifying subcentimeter lesions in particular.

A discriminative distance learning---based CBIR framework for characterization of indeterminate liver lesions

In this paper we propose a novel learning---based CBIR method for fast content---based retrieval of similar 3D images based on the intrinsic Random Forest (RF) similarity. Furthermore, we allow the combination of flexible user---defined semantics (in the form of retrieval contexts and high---level concepts) and appearance---based (low---level) features in order to yield search results that are both meaningful to the user and relevant in the given clinical case. Due to the complexity and clinical relevance of the domain, we have chosen to apply the framework to the retrieval of similar 3D CT hepatic pathologies, where search results based solely on similarity of low---level features would be rarely clinically meaningful. The impact of high---level concepts on the quality and relevance of the retrieval results has been measured and is discussed for three different set---ups. A comparison study with the commonly used canonical Euclidean distance is presented and discussed as well.

A method for mass candidate detection and an application to liver lesion detection

Detection and segmentation of abnormal masses within organs in Computed Tomography (CT) images of patients is of practical importance in computer-aided diagnosis (CAD), treatment planning, and analysis of normal as well as pathological regions. For intervention planning e.g. in radiotherapy the detection of abnormal masses is essential for patient diagnosis, personalized treatment choice and follow-up. The unpredictable nature of disease often makes the detection of the presence, appearance, shape, size and number of abnormal masses a challenging task, which is particularly tedious when performed by hand. Moreover, in cases in which the imaging protocol specifies the administration of a contrast agent, the contrast agent phases at which the patient images are acquired have a dramatic influence on the shape and appearance of the diseased masses. In this paper we propose a method to automatically detect candidate lesions (CLs) in 3D CTs of liver lesions. We introduce a novel multilevel candidate generation method that proves clearly advantageous in a comparative study with a state of the art approach. A learning-based selection module and a candidate fusion module are then introduced to reduce both redundancy and the false positive rate. The proposed workflow is applied to the detection of both hyperdense and hypodense hepatic lesions in all contrast agent phases, with resulting sensitivities of 89.7% and 92% and positive predictive values of 82.6% and 87.6% respectively.