Automatic colon segmentation with dual scan CT colonography

Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network

Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps' segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.

Colon centerline extraction in fragmented segmentations

In virtual colonoscopy, the clinical need is a smooth centered path from the rectum to the cecum, for interactive navigation along the colonic lumen. The primary challenge is breakages in the colon, due to fecal residue, abnormalities, poor insufflation and inadequate electronic cleansing. Here we propose a method, that is a modification of the classic energy minimized geodesic, that extracts centered paths through fragmented colons. To begin, we perform electronic cleansing, automatically localize 4 points: rectum, cecum, sphlenic and hepatic flexures; followed by region growing and heuristic approaches to generate the initial segmentation. This is followed by a daisy chaining procedure to link possibly large colon blobs that may have been missed as weaker candidate segmentations. We then perform a front propagation to extract a minimal energy path through the ordered set of points. This propagation is guided by multiple forces: (a) A strong force given by the distance to the colon segmentation surface (b) A weak force derived from the CT intensity (c) A weak force from the distance to the surface of weaker candidate colon segmentations (d) A geodesic repulsive force, where the other points exhibit an repelling force in their voronoi partition, the force proportional to the geodesic distance to the point. Our contribution is a path extraction method for the colon that is the energy minimized geodesic (a) favouring centeredness (b) punching through gaps, traversing in so far as possible through lower intensity regions and possibly centered within these gaps (c) ordered through the feature points. Results show improvements of the method over the standard minimal energy path approach.

Automatic region-of-interest segmentation and registration of dynamic contrast-enhanced images of colorectal tumors

Dynamic contrast-enhanced (DCE) images can be acquired at multiple time points and multiple slice locations of a tumor. Image segmentation and registration are important preprocessing steps that can improve subsequent analysis of DCE images by kinetic modeling. An automatic system for region-of-interest segmentation and registration of DCE images is presented. Tissue segmentation is performed using a combination of thresholding and morphological operations, and further refined using shape information from consecutive images. The segmented regions are subsequently registered based on a mutual information method that accounts for possible tissue movement between slices. The proposed segmentation and registration methods are applied on actual DCE CT datasets to illustrate feasibility of practical implementation in the clinic.

Semi-automated segmentation of the sigmoid and descending colon for radiotherapy planning using the fast marching method

A fast and accurate segmentation of organs at risk, such as the healthy colon, would be of benefit for planning of radiotherapy, in particular in an adaptive scenario. For the treatment of pelvic tumours, a great challenge is the segmentation of the most adjacent and sensitive parts of the gastrointestinal tract, the sigmoid and descending colon. We propose a semi-automated method to segment these bowel parts using the fast marching (FM) method. Standard 3D computed tomography (CT) image data obtained from routine radiotherapy planning were used. Our pre-processing steps distinguish the intestine, muscles and air from connective tissue. The core part of our method separates the sigmoid and descending colon from the muscles and other segments of the intestine. This is done by utilizing the ability of the FM method to compute a specified minimal energy functional integrated along a path, and thereby extracting the colon centre line between user-defined control points in the sigmoid and descending colon. Further, we reconstruct the tube-shaped geometry of the sigmoid and descending colon by fitting ellipsoids to points on the path and by adding adjacent voxels that are likely voxels belonging to these bowel parts. Our results were compared to manually outlined sigmoid and descending colon, and evaluated using the Dice coefficient (DC). Tests on 11 patients gave an average DC of 0.83 (+/-0.07) with little user interaction. We conclude that the proposed method makes it possible to fast and accurately segment the sigmoid and descending colon from routine CT image data.

Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial basis function networks

As being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method uses a Self Generating Hierarchical Radial Basis Function Network to determine the lobes of a Volume Histogram Stack (VHS) which is introduced as a new domain by aligning the histograms of slices of a image series. The new self generating hierarchical design strategy allows the recognition of suppressed lobes corresponding to suppressed tissues and representation of the overlapping regions which are parts of the lobes but can not be represented by the Gaussian bases in VHS. Moreover, approximation with a minimum set of basis functions provides the possibility of selecting and adjusting suitable units to optimize the TF. Applications on different CT and MR data sets show enhanced rendering quality and reduced optimization time in abdominal studies.

An automatic method for colon segmentation in CT colonography

An automatic method for the segmentation of the colonic wall is proposed for abdominal computed tomography (CT) of the cleansed and air-inflated colon. This multistage approach uses an adaptive 3D region-growing algorithm, with a self-adjusting growing condition depending on local variations of the intensity at the air-tissue boundary. The method was evaluated using retrospectively collected CT scans based on visual segmentation of the colon by expert radiologists. This evaluation showed that the procedure identifies 97% of the colon segments, representing 99.8% of the colon surface, and accurately replicates the anatomical profile of the colonic wall. The parameter settings and performance of the method are relatively independent of the scanner and acquisition conditions. The method is intended for application to the computer-aided detection of polyps in CT colonography.

CAD in CT colonography without and with oral contrast agents: progress and challenges

Computed tomographic colonography (CTC), also known as virtual colonoscopy, is an emerging alternative technique for screening of colon cancers. CTC uses CT to provide a series of cross-sectional images of the colon for detection of polyps and masses. Fecal tagging is a means of labeling of residual feces by an oral contrast agent for improving the accuracy in the detection of polyps. Computer-aided diagnosis (CAD) for CTC automatically determines the locations of suspicious polyps and masses in CTC and presents them to radiologists, typically as a second opinion. Despite its relatively short history, CAD has become one of the mainstream techniques that could make CTC prime time for screening of colorectal cancer. Rapid technical developments have advanced CAD substantially during the last several years, and a fundamental scheme for the detection of polyps has been established, in which sophisticated 3D image processing, analysis, and display techniques play a pivotal role. The latest CAD systems indicate a clinically acceptable high sensitivity and a low false-positive rate, and observer studies have demonstrated the benefits of these systems in improving radiologists' detection performance. Some technical and clinical challenges, however, remain unresolved before CAD can become a truly useful tool for clinical practice. Also, new challenges are facing CAD as the methods for bowel preparation and image acquisition, such as tagging of fecal residue with oral contrast agents, and interpretation of CTC images evolve. This article reviews the current status and future challenges in CAD for CTC without and with fecal tagging.