ARR-GCN: Anatomy-Relation Reasoning Graph Convolutional Network for Automatic Fine-Grained Segmentation of Organ's Surgical Anatomy.

Anatomical resection (AR) based on anatomical sub-regions is a promising method of precise surgical resection, which has been proven to improve long-term survival by reducing local recurrence. The fine-grained segmentation of an organ's surgical anatomy (FGS-OSA), i.e., segmenting an organ into multiple anatomic regions, is critical for localizing tumors in AR surgical planning. However, automatically obtaining FGS-OSA results in computer-aided methods faces the challenges of appearance ambiguities among sub-regions (i.e., inter-sub-region appearance ambiguities) caused by similar HU distributions in different sub-regions of an organ's surgical anatomy, invisible boundaries, and similarities between anatomical landmarks and other anatomical information. In this paper, we propose a novel fine-grained segmentation framework termed the "anatomic relation reasoning graph convolutional network" (ARR-GCN), which incorporates prior anatomic relations into the framework learning. In ARR-GCN, a graph is constructed based on the sub-regions to model the class and their relations. Further, to obtain discriminative initial node representations of graph space, a sub-region center module is designed. Most importantly, to explicitly learn the anatomic relations, the prior anatomic-relations among the sub-regions are encoded in the form of an adjacency matrix and embedded into the intermediate node representations to guide framework learning. The ARR-GCN was validated on two FGS-OSA tasks: i) liver segments segmentation, and ii) lung lobes segmentation. Experimental results on both tasks outperformed other state-of-the-art segmentation methods and yielded promising performances by ARR-GCN for suppressing ambiguities among sub-regions.

Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.

BACKGROUND AND PURPOSE: Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to develop a dose prediction deep learning method for nasopharyngeal carcinoma radiotherapy by taking advantage of the distance information as well as the mask information. MATERIALS AND METHODS: A novel transformation method based on boundary distance was proposed to facilitate the prediction of dose distributions. Radiotherapy datasets of 161 nasopharyngeal carcinoma patients were retrospectively collected, including binary masks of organs-at-risk (OARs) and targets, planning CT, and clinical plans. The patients were randomly divided into 130, 11 and 20 cases for training, validating, and testing the models, respectively. Furthermore, 40 patients from an external cohort were used to test the generalizability of the models. RESULTS: The proposed method shows superior performance. The predicted dose error and dose-volume histogram (DVH) error of our method were 7.51% and 11.6% lower than the mask-based method, respectively. For the inverse planning, compared with mask-based methods, our method provided similar performances on the GTVnx and OARs and outperformed on the GTVnd and the CTV, the pass rates of which increased from 89.490% and 90.016% to 96.694% and 91.189%, respectively. CONCLUSION: The preliminary results on nasopharyngeal carcinoma radiotherapy cases showed that our proposed distance-guided method for dose prediction achieved better performance than mask-based methods. Further studies with more patients and on other cancer sites are warranted to fully validate the proposed method.

[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.