Olga Zinoveva

Bio: Olga Zinoveva is an academic researcher from Harvard University. The author has contributed to research in topics: Ground truth & Scale-space segmentation. The author has an hindex of 2, co-authored 2 publications receiving 18 citations.

A texture-based probabilistic approach for lung nodule segmentation

Abstract: Producing consistent segmentations of lung nodules in CT scans is a persistent problem of image processing algorithms. Many hard-segmentation approaches are proposed in the literature, but soft segmentation of lung nodules remains largely unexplored. In this paper, we propose a classification-based approach based on pixel-level texture features that produces soft (probabilistic) segmentations. We tested this classifier on the publicly available Lung Image Database Consortium (LIDC) dataset. We further refined the classification results with a post-processing algorithm based on the variability index. The algorithm performed well on nodules not adjacent to the chest wall, producing a soft overlap between radiologists' based segmentation and computer-based segmentation of 0.52. In the long term, these soft segmentations will be useful for representing the uncertainty in nodule boundaries that is manifest in radiological image segmentations.

A shape-dependent variability metric for evaluating panel segmentations with a case study on LIDC

Abstract: The segmentation of medical images is challenging because a ground truth is often not available. Computer-Aided Detection (CAD) systems are dependent on ground truth as a means of comparison; however, in many cases the ground truth is derived from only experts’ opinions. When the experts disagree, it becomes impossible to discern one ground truth. In this paper, we propose an algorithm to measure the disagreement among radiologist’s delineated boundaries. The algorithm accounts for both the overlap and shape of the boundaries in determining the variability of a panel segmentation. After calculating the variability of 3788 thoracic computed tomography (CT) slices in the Lung Image Database Consortium (LIDC), we found that the radiologists have a high consensus in a majority of lung nodule segmentations. However, our algorithm identified a number of segmentations that the radiologists significantly disagreed on. Our proposed method of measuring disagreement can assist others in determining the reliability of panel segmentations. We also demonstrate that it is superior to simply using overlap, which is currently one of the most common ways of measuring segmentation agreement. The variability metric presented has applications to panel segmentations, and also has potential uses in CAD systems.

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

Abstract: This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems

A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise.

Abstract: The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise (DBSCAN). First, our method uses three-dimensional computed tomography image features of the average intensity projection combined with multi-scale dot enhancement for preprocessing. Hexagonal clustering and morphological optimized sequential linear iterative clustering (HMSLIC) for sequence image oversegmentation is then proposed to obtain superpixel blocks. The adaptive weight coefficient is then constructed to calculate the distance required between superpixels to achieve precise lung nodules positioning and to obtain the subsequent clustering starting block. Moreover, by fitting the distance and detecting the change in slope, an accurate clustering threshold is obtained. Thereafter, a fast DBSCAN superpixel sequence clustering algorithm, which is optimized by the strategy of only clustering the lung nodules and adaptive threshold, is then used to obtain lung nodule mask sequences. Finally, the lung nodule image sequences are obtained. The experimental results show that our method rapidly, completely and accurately segments various types of lung nodule image sequences.

A Review on Image Processing Methods in Detecting Lung Cancer Using CT Images

Abstract: Lung cancer is the most common cancer for death among all cancers and CT scan is the best modality for imaging lung cancer. A good amount of research work has been carried out in the past towards CAD system for lung cancer detection using CT images. It is divided into four stages. They are pre-processing or lung segmentation, nodule detection, nodule segmentation and classification. This paper presents in detail literature survey on various techniques that have been used in Pre-processing, nodule segmentation and classification.

Weak Segmentations and Ensemble Learning to Predict Semantic Ratings of Lung Nodules

Abstract: Computer-aided diagnosis (CAD) can be used as "second readers" in the imaging diagnostic process. Typically to create a CAD system, the region of interest (ROI) has to be first detected and then delineated. This can be done either manually or automatically. Given that manually delineating ROIs is a time consuming and costly process, we propose a CAD system based on multiple computer-derived weak segmentations (WSCAD) and show that its diagnosis performance is at least as good as the predictions developed using manual radiologist segmentations. The proposed CAD system extracts a set of image features from the weak segmentations and uses them in an ensemble of classification algorithms to predict semantic ratings such as malignancy. These automated results are compared against a reference truth based on ratings and segmentations provided by radiologists to determine if it is necessary to obtain manual radiologist segmentations in order to develop a CAD. By developing a pair of CADs using the Lung Image Database Consortium (LIDC) data, we show that WSCADs are at least as accurate in predicting semantic ratings as CADs based on radiologist segmentation.

A texture-based probabilistic approach for lung nodule segmentation

Abstract: Producing consistent segmentations of lung nodules in CT scans is a persistent problem of image processing algorithms. Many hard-segmentation approaches are proposed in the literature, but soft segmentation of lung nodules remains largely unexplored. In this paper, we propose a classification-based approach based on pixel-level texture features that produces soft (probabilistic) segmentations. We tested this classifier on the publicly available Lung Image Database Consortium (LIDC) dataset. We further refined the classification results with a post-processing algorithm based on the variability index. The algorithm performed well on nodules not adjacent to the chest wall, producing a soft overlap between radiologists' based segmentation and computer-based segmentation of 0.52. In the long term, these soft segmentations will be useful for representing the uncertainty in nodule boundaries that is manifest in radiological image segmentations.