Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut

Abstract

We propose an automatic multiorgan segmentation method for 3-D radiological images of different anatomical contents and modalities. The approach is based on a simultaneous multilabel graph cut optimization of location, appearance, and spatial configuration criteria of target structures. Organ location is defined by target-specific probabilistic atlases (PA) constructed from a training dataset using a fast (2+1)D SURF-based multiscale registration method involving a simple four-parameter transformation. PAs are also used to derive target-specific organ appearance models represented as intensity histograms. The spatial configuration prior is derived from shortest-path constraints defined on the adjacency graph of structures. Thorough evaluations on Visceral project benchmarks and training dataset, as well as comparisons with the state-of-the-art confirm that our approach is comparable to and often outperforms similar approaches in multiorgan segmentation, thus proving that the combination of multiple suboptimal but complementary information sources can yield very good performance.

Local Surf-Based Keypoint Transfer Segmentation

“…We compare our segmentation method with two related ones on the VISCERAL training dataset: [9], which is a multiatlas algorithm using a keypoint-based registration approach, and the original keypoint transfer algorithm [3].…”

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

“…Keypoint-based registration is a popular research direction in feature-based registration methods [45,46]. Generally, the Harris algorithm, scale invariant feature transform (SIFT) algorithm, and the speeded-up robust features (SURF) algorithm are used to extract keypoints, and most contemporary studies focus on these algorithms for improvement [47][48][49]. Registration methods based on keypoints primarily include keypoint acquisition, keypoint matching, transformation parameter solving, and transformation model optimization [50].…”

An Improved Matrix Factorization Based Active Contours Combining Edge Preservation for Image Segmentation

“…Over the past decades, many image segmentation algorithms, including wavelet transformation [1][2], graph cut [3][4], edge detection [5][6], level set [7][8], deep learning [9][10], have been presented. Among them, active contour models (ACMs) based on level set theory have become a successful branch.…”