3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images

Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan

Many different diseases can occur in the liver, including infections such as hepatitis, cirrhosis, cancer and over effect of medication or toxins. The foremost stage for computer-aided diagnosis of liver is the identification of liver region. Liver segmentation algorithms extract liver image from scan images which helps in virtual surgery simulation, speedup the diagnosis, accurate investigation and surgery planning. The existing liver segmentation algorithms try to extort exact liver image from abdominal Computed Tomography (CT) scan images. It is an open problem because of ambiguous boundaries, large variation in intensity distribution, variability of liver geometry from patient to patient and presence of noise. A novel approach is proposed to meet challenges in extracting the exact liver image from abdominal CT scan images. The proposed approach consists of three phases: (1) Pre-processing (2) CT scan image transformation to Neutrosophic Set (NS) and (3) Post-processing. In pre-processing, the noise is removed by median filter. The "new structure" is designed to transform a CT scan image into neutrosophic domain which is expressed using three membership subset: True subset (T), False subset (F) and Indeterminacy subset (I). This transform approximately extracts the liver image structure. In post processing phase, morphological operation is performed on indeterminacy subset (I) and apply Chan-Vese (C-V) model with detection of initial contour within liver without user intervention. This resulted in liver boundary identification with high accuracy. Experiments show that, the proposed method is effective, robust and comparable with existing algorithm for liver segmentation of CT scan images.

Automatic liver segmentation based on appearance and context information

BACKGROUND

Automated image segmentation has benefits for reducing clinicians' workload, quicker diagnosis, and a standardization of the diagnosis.

METHODS

This study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction.

RESULTS

The proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system.

CONCLUSIONS

Experimental results show that the proposed method is superior to eight other state of the art methods.

Automatic 3D liver location and segmentation via convolutional neural network and graph cut

PURPOSE

Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans.

METHODS

The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map.

RESULTS

The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively.

CONCLUSIONS

The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.