Automatic Liver Tumor Segmentation Research Articles

Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing

Automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up assessment, thanks to standardization and incorporation of full volumetric information. In this work, we develop a fully automatic method for liver tumor segmentation in CT images based on a 2D fully convolutional neural network with an object-based postprocessing step. We describe our experiments on the LiTS challenge training data set and evaluate segmentation and detection performance. Our proposed design cascading two models working on voxel- and object-level allowed for a significant reduction of false positive findings by 85% when compared with the raw neural network output. In comparison with the human performance, our approach achieves a similar segmentation quality for detected tumors (mean Dice 0.69 vs. 0.72), but is inferior in the detection performance (recall 63% vs. 92%). Finally, we describe how we participated in the LiTS challenge and achieved state-of-the-art performance.

A review of automatic liver tumor segmentation based on computed tomography

Liver cancer is a common type of malignant tumor in digestive system. At present, computed tomography (CT) plays an important role in the diagnosis and treatment of liver cancer. Segmentation of tumor lesions based on CT is thus critical in clinical diagnosis and treatment. Due to the limitations of manual segmentation, such as inefficiency and subjectivity, the automatic and accurate segmentation based on advanced computational techniques is becoming more and more popular. In this review, we summarize the research progress of automatic segmentation of liver cancer lesions based on CT scans. By comparing and analyzing the results of experiments, this review evaluate various methods objectively, so that researchers in related fields can better understand the current research progress of liver cancer segmentation based on CT scans.

Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring.

Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset.

Statistical Parameter-based Automatic Liver Tumor Segmentation from Abdominal CT Scans: A Potiential Radiomic Signature

Liver imaging using abdominal CT images has been widely studied in the recent years and is a challenging task. Processing CT image includes the automatic diagnosis of liver and lesions part. Because of the high intensity similarity between liver tissues and nearby organs of liver it is difficult to segment liver and tumor. Segmentation of extracted region as an imaging biomarker forms an essential component of “Radiomics”. This paper presents automatic liver tumor segmentation from abdominal CT scan images. A statistical parameter-based approach is used to distinguish liver tumor tissue from other abdominal organs. The existing segmentation methods such as region growing and intensity based thresholding methods are investigated. First, the CT images are preprocessed by filtering to remove noise from the image. Then the statistical mean-based thresholding is applied to extract the tumor. After applying median filtering, isodata threshold is used to turn the image into binary with tumors as black spots on white background. Finally postprocessing as filtering techniques like mean filter and median filter and morphological operations are performed to remove residues. This paper highlights liver tumor segmentation analysis which has the potential as a imaging biomarker for “Personalised cancer imaging”.

Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation

We present a method and a validation study for the nearly automatic segmentation of liver tumors in CTA scans. Our method inputs a liver CTA scan and a small number of user-defined seeds. It first classifies the liver voxels into tumor and healthy tissue classes with an SVM classification engine from which a new set of high- quality seeds is generated. Next, an energy function describing the propagation of these seeds is defined over the 3D image. The functional consists of a set of linear equations that are optimized with the conjugate gradients method. The result is a continuous segmentation map that is thresholded to obtain a binary segmentation. A retrospective study on a validated clinical dataset consisting of 20 tumors from nine patients' CTA scans from the MICCAI'08 3D Liver Tumors Segmentation Challenge Workshop yielded an average aggregate score of 67, an average symmetric surface distance of 1.76mm (SD = 0.61mm) which is better than the 2.0mm of other methods on the same database, and a comparable volumetric overlap error (33.8 vs. 32.6%). The advantage of our method is that it requires less user interaction compared to other methods. Our results indicate that our method is accurate, efficient, and robust to wide variety of tumor types and is comparable or superior to other semi-automatic segmentation methods, with much less user interaction.

Liver Tumor Segmentation Using Implicit Surface Evolution

A method for automatic liver tumor segmentation from computer tomography (CT) images is presented in this paper. Segmentation is an important operation before surgery planning, and automatic methods offer an alternative to laborious manual segmentation. In addition, segmentations of automatic methods are reproducible, so they can be reliably evaluated and they do not depend on the performer of the segmentation. In this work, the segmentation is performed in two stages. First a rough segmentation of tumors is obtained by simple thresholding and morphological operations. The second stage refines the rough segmentation result using fuzzy clustering and a geometric deformable model (GDM) that is fitted on the clustering result. The method was evaluated with data provided by Liver Tumor Segmentation Challenge 08, to which the method also participated. The data included 10 images from which 20 tumors were segmented. The method showed promising results.