AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

• URL: http://arxiv.org/abs/2010.14808v2
• Date: Wed, 21 Jul 2021 03:36:11 GMT
• Title: AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
• Authors: Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, Xiaoping Yang
• Abstract summary: This paper presents a large and diverse abdominal CT organ segmentation dataset, AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers. We conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods. To advance the unsolved problems, we build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning.
• Score: 30.338209680140913
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods. The datasets, codes, and trained models are publicly available at https://github.com/JunMa11/AbdomenCT-1K.

Related papers

• Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines [113.08940153125616]
  We generate a dataset of whole-body CT scans with $142$ voxel-level labels for 533 volumes providing comprehensive anatomical coverage. Our proposed procedure does not rely on manual annotation during the label aggregation stage. We release our trained unified anatomical segmentation model capable of predicting $142$ anatomical structures on CT data.
  arXiv  Detail & Related papers  (2023-07-25T09:48:13Z)
• Boundary-Aware Network for Abdominal Multi-Organ Segmentation [21.079667938055668]
  We propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans. The results demonstrate that BA-Net is superior to nnUNet on both segmentation tasks.
  arXiv  Detail & Related papers  (2022-08-29T02:24:02Z)
• AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation [32.938687630678096]
  AMOS is a large-scale, diverse, clinical dataset for abdominal organ segmentation. It provides challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset.
  arXiv  Detail & Related papers  (2022-06-16T09:27:56Z)
• External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data [72.99534552950138]
  The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. accurate segmentation of splenic vascular injury is challenging for the following reasons.
  arXiv  Detail & Related papers  (2022-01-04T02:35:56Z)
• WORD: Revisiting Organs Segmentation in the Whole Abdominal Region [14.752924082744814]
  Whole abdominal organs segmentation plays an important role in abdomen lesion diagnosis, radiotherapy planning, and follow-up. Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training. In this work, we establish a large-scale textitWhole abdominal textitORgans textitDataset (textitWORD) for algorithms research and clinical applications development.
  arXiv  Detail & Related papers  (2021-11-03T02:26:14Z)
• FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images [82.48587399026319]
  delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. We propose a novel two-stage deep neural network, FocusNetv2, to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs. In addition to our original FocusNet, we employ a novel adversarial shape constraint on small organs to ensure the consistency between estimated small-organ shapes and organ shape prior knowledge.
  arXiv  Detail & Related papers  (2021-04-05T04:45:31Z)
• RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior [4.177877537413942]
  coarse-to-fine abdominal multi-organ segmentation facilitates to extract high-resolution segmentation. We propose a single refined model to segment all abdominal organs instead of multiple organ corresponding models. Our proposed method outperforms the state-of-the-art on 13 models with an average Dice score 84.58% versus 81.69% (p0.0001)
  arXiv  Detail & Related papers  (2020-12-23T00:22:05Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)
• Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes [52.53375964591765]
  We propose the first fully-automated solution to segment the whole fetal head in US volumes. The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
  arXiv  Detail & Related papers  (2020-04-28T14:43:05Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)
• CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation [7.429032855333529]
  CHAOS provides both abdominal CT and MR data from healthy subjects for single and multiple abdominal organ segmentation. Five different but complementary tasks have been designed to analyze the capabilities of current approaches from multiple perspectives. The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0.98 $pm$ 0.00 / 0.95 $pm$ 0.01) but the best MSSD performance remain limited (21.89 $pm$ 13.94 / 20.85 $pm$ 10.63 mm)
  arXiv  Detail & Related papers  (2020-01-17T21:11:46Z)

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.