Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

• URL: http://arxiv.org/abs/2108.00911v2
• Date: Thu, 5 Aug 2021 09:45:58 GMT
• Title: Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
• Authors: Yue Zhang, Chengtao Peng, Liying Peng, Huimin Huang, Ruofeng Tong, Lanfen Lin, Jingsong Li, Yen-Wei Chen, Qingqing Chen, Hongjie Hu, Zhiyi Peng
• Abstract summary: Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS) We propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation.
• Score: 12.343161202847018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features through phase-weighted summation or channel-attention based concatenation. However, these methods ignored the spatial (pixel-wise) relationships between different phases, hence leading to insufficient feature integration. In addition, the performance of existing methods remains subject to the uncertainty in segmentation, which is particularly acute in tumor boundary regions. In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation. To this end, we introduce a spatial aggregation module (SAM), which encourages per-pixel interactions between different phases, to make full use of cross-phase information. Moreover, we devise an uncertain region inpainting module (URIM) to refine uncertain pixels using neighboring discriminative features. Experiments on an in-house multi-phase CT dataset of focal liver lesions (MPCT-FLLs) demonstrate that our method achieves promising liver tumor segmentation and outperforms state-of-the-arts.

Related papers

• OmniCT: Towards a Unified Slice-Volume LVLM for Comprehensive CT Analysis [53.01523944168442]
  Clinical interpretation relies on both slice-driven local features and volume-driven spatial representations.<n>Existing Large Vision-Language Models (LVLMs) remain fragmented in CT slice versus volumetric understanding.<n>We present OmniCT, a powerful unified slice-volume LVLM for CT scenarios.
  arXiv  Detail & Related papers  (2026-02-18T00:42:41Z)
• A Multi-scale Fused Graph Neural Network with Inter-view Contrastive Learning for Spatial Transcriptomics Data Clustering [7.214595408714774]
  stMFG is proposed, a multi-scale interactive fusion graph network that introduces layer-wise cross-view attention to dynamically integrate spatial and gene features after each convolution.<n>It outperforms state-of-the-art methods, achieving up to 14% ARI improvement on certain slices.
  arXiv  Detail & Related papers  (2025-12-18T05:13:55Z)
• Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI [6.755224757651558]
  We propose a label-efficient segmentation approach that promotes cross-modality generalization under real-world conditions.<n>Our method integrates a foundation-scale 3D segmentation backbone adapted via fine-tuning, co-training with cross pseudo supervision to leverage unlabeled volumes.<n>Our results exhibit the effectiveness of our proposed label-efficient baseline for liver segmentation in multi-phase, multi-vendor MRI.
  arXiv  Detail & Related papers  (2025-10-06T11:19:05Z)
• Unified Multimodal Coherent Field: Synchronous Semantic-Spatial-Vision Fusion for Brain Tumor Segmentation [13.108816349958659]
  We propose the Unified Multimodal Coherent Field (UMCF) method for brain tumor segmentation.<n>Method achieves synchronous interactive fusion of visual, semantic, and spatial information within a unified 3D space.<n>On Brain Tumor (BraTS) datasets, UMCF+Unn-Net achieves average Dice of 0.8579 and 0.8977 respectively, with an average 4.18% improvement across mainstream architectures.
  arXiv  Detail & Related papers  (2025-09-22T08:45:39Z)
• Enhanced MRI Representation via Cross-series Masking [48.09478307927716]
  Cross-Series Masking (CSM) Strategy for effectively learning MRI representation in a self-supervised manner. Method achieves state-of-the-art performance on both public and in-house datasets.
  arXiv  Detail & Related papers  (2024-12-10T10:32:09Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty [6.631670005069929]
  Multi-phase liver tumor segmentation (MPLiTS)-based methods suffer from redundancy and weak interpretability. We propose a novel trustworthy multi-phase liver tumor segmentation (TMPLiTS) TMPLiTS is a unified framework jointly conducting segmentation and uncertainty estimation.
  arXiv  Detail & Related papers  (2023-05-09T11:10:51Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module [14.021944194533644]
  Hard exudates (HE) is the most specific biomarker for retina edema. This paper proposes a novel hard exudates segmentation method named SS-MAF with an auxiliary super-resolution task. We evaluate our method on two public lesion datasets, IDRiD and E-Ophtha.
  arXiv  Detail & Related papers  (2022-11-17T08:25:04Z)
• Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images [22.128902125820193]
  We propose a Decoupled Pyramid Correlation Network (DPC-Net) It exploits attention mechanisms to fully leverage both low and high-level features embedded in FCN to segment liver tumor. It achieves a competitive results with a DSC of 96.2% and an ASSD of 1.636 mm for liver segmentation.
  arXiv  Detail & Related papers  (2022-05-26T07:31:29Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation [92.9634065964963]
  We present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet) based on our uncertainty estimation and separate self-training strategy. Compared with the current state of the art, our CoraNet has demonstrated superior performance.
  arXiv  Detail & Related papers  (2021-10-17T08:49:33Z)
• Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images [2.5576691384612413]
  We propose a novel context-aware PolyUNet for accurate liver and lesion segmentation. It jointly explores structural diversity and consecutive t-adjacent slices to enrich feature expressive power and spatial contextual information. Our method achieved very competitive performance at the MICCAI 2017 Liver Tumor (LiTS) Challenge.
  arXiv  Detail & Related papers  (2021-06-21T18:01:04Z)
• Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning [0.12891210250935145]
  We propose a new iterative refined interactive segmentation method for medical images based on agent reinforcement learning. We model the dynamic process of drawing the target contour in a certain order as a Markov Decision Process (MDP) based on a deep reinforcement learning method. Experimental results show that our method has a better segmentation effect on the left ventricle in a small number of medical image data sets.
  arXiv  Detail & Related papers  (2021-06-08T06:30:32Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)
• Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion [71.87627318863612]
  We propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities. Our network uses feature disentanglement to decompose the input modalities into the modality-specific appearance code. We validate our method on the important yet challenging multimodal brain tumor segmentation task with the BRATS challenge dataset.
  arXiv  Detail & Related papers  (2020-02-22T14:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.