Living Donor-Recipient Pair Matching for Liver Transplant via Ternary Tree Representation With Cascade Incremental Learning

Visual understanding of liver vessels anatomy between the living donor-recipient (LDR) pair can assist surgeons to optimize transplant planning by avoiding non-targeted arteries which can cause severe complications. We propose to visually analyze the anatomical variants of the liver vessels anatomy to maximize similarity for finding a suitable Living Donor-Recipient (LDR) pair. Liver vessels are segmented from computed tomography angiography (CTA) volumes by employing a cascade incremental learning (CIL) model. Our CIL architecture is able to find optimal solutions, which we use to update the model with liver vessel CTA images. A novel ternary tree based algorithm is proposed to map all the possible liver vessel variants into their respective tree topologies. The tree topologies of the recipient's and donor's liver vessels are then used for an appropriate matching. The proposed algorithm utilizes a set of defined vessel tree variants which are updated to maintain the maximum matching options by leveraging the accurate segmentation results of the vessels derived from the incremental learning ability of the CIL. We introduce a novel concept of in-order digital string based comparison to match the geometry of two anatomically varied trees. Experiments through visual illustrations and quantitative analysis demonstrated the effectiveness of our approach compared to state-of-the-art.

Linear measurements of sinus floor elevation based on voxel-based superimposition of cone beam computed tomography images

BACKGROUND

Postsurgical evaluation of sinus floor elevation regularly involves linear measurements of the elevated volumes in the cone beam computed tomography (CBCT) images. The accuracy of measurements could be compromised due to ill-defined sinus floor outline if implants are placed simultaneously.

PURPOSE

The aim was to examine a CBCT superimposition method to improve the measurement accuracy.

MATERIALS AND METHODS

Twenty patients who received transalveolar sinus floor elevation with immediate implantation were enrolled. CBCTs before and after surgery were transformed into digital imaging and communications in medicine format and imported into the Dolphin Imaging software. Voxel-based superimposition was automated to merge the files. In the superimposed image, parameters including alveolar bone height, protruded implant length, and total elevated height were measured. The superimposition and measurements were performed independently by two examiners and in two timepoints with 1-week time interval. We used intraclass correlation coefficient (ICC) to analyze the interexaminer and intraexaminer agreements.

RESULTS

Of measured parameters, the mean of difference between two timepoints ranged from 0.18 to 0.26 mm by examiner 1, and from 0.16 to 0.20 mm by examiner 2. ICCs were equal or greater than 0.98, indicating perfect intraexaminer agreement. For interexaminer reliability, the largest mean of difference was 0.27 mm in measuring alveolar bone height between two examiners. ICCs were greater than 0.98, showing perfect interexaminer agreement.

CONCLUSIONS

The voxel-based superimposition of pre- and post-surgical CBCT images with Dolphin Imaging is an effective and reliable way for linear measurements so as to assess the surgical outcome. There is minimal effect on reproducibility of measured data by different timepoints or performers.

Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images

An efficient and precise liver extraction from computed tomography (CT) images is a crucial step for computer-aided hepatic diseases diagnosis and treatment. Considering the possible risk to patient's health due to X-ray radiation of repetitive CT examination, low-dose CT (LDCT) is an effective solution for medical imaging. However, inhomogeneous appearances and indistinct boundaries due to additional noise and streaks artifacts in LDCT images often make it a challenging task. This study aims to extract a liver model from LDCT images for facilitating medical expert in surgical planning and post-operative assessment along with low radiation risk to the patient. Our method carried out liver extraction by employing residual convolutional neural networks (LER-CN), which is further refined by noise removal and structure preservation components. After patch-based training, our LER-CN shows a competitive performance relative to state-of-the-art methods for both clinical and publicly available MICCAI Sliver07 datasets. We have proposed training and learning algorithms for LER-CN based on back propagation gradient descent. We have evaluated our method on 150 abdominal CT scans for liver extraction. LER-CN achieves dice similarity coefficient up to 96.5[Formula: see text], decreased volumetric overlap error up to 4.30[Formula: see text], and average symmetric surface distance less than 1.4 [Formula: see text]. These findings have shown that LER-CN is a favorable method for medical applications with high efficiency allowing low radiation risk to patients.