Liver Segmentation

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

29 shown of 29 papers with code (86 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Med3D: Transfer Learning for 3D Medical Image Analysis
  1 Apr 2019 7 repositories listed
  The performance on deep learning is significantly affected by volume of training data.
• The Liver Tumor Segmentation Benchmark (LiTS)
  13 Jan 2019 6 repositories listed Syntology ran 0 of 8 samples · 8 unverified
  In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the…
• Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
  2 Dec 2021 2 repositories listed
  The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis.
• Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
  14 Jul 2020 2 repositories listed
  To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a…
• Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
  19 Aug 2019 2 repositories listed
  More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including…
• Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
  5 Jul 2019 2 repositories listed
  We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images.
• H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
  21 Sep 2017 2 repositories listed
  Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
• Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images
  14 Apr 2025 1 repository listed
  Accurate segmentation of abdominal adipose tissue, including subcutaneous (SAT) and visceral adipose tissue (VAT), along with liver segmentation, is essential for understanding body composition and associated health…
• A Reverse Mamba Attention Network for Pathological Liver Segmentation
  23 Feb 2025 1 repository listed
  The architecture's generalizability is further validated on the cancerous liver segmentation from CT scans (LiTS: Liver Tumor Segmentation dataset), yielding a Dice score of 92.
• SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing
  13 Oct 2024 1 repository listed
  Liver cancer is a leading cause of mortality worldwide, and accurate Computed Tomography (CT)-based tumor segmentation is essential for diagnosis and treatment.
• CT Liver Segmentation via PVT-based Encoding and Refined Decoding
  17 Jan 2024 1 repository listed
  Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
• From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation
  31 Oct 2023 1 repository listed
  In medical image segmentation, domain generalization poses a significant challenge due to domain shifts caused by variations in data acquisition devices and other factors.
• Mci-net: multi-scale context integrated network for liver ct image segmentation
  3 May 2023 1 repository listed
  Owing to the various object scales and high similarity with the surrounding organs (e.
• Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
  20 Jul 2022 1 repository listed
  This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the…
• Transformer based Generative Adversarial Network for Liver Segmentation
  21 May 2022 1 repository listed
  The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.
• Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion
  4 Feb 2022 1 repository listed
  For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image.
• OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data
  20 Oct 2021 1 repository listed
  Occupancy networks (O-Nets) are an alternative for which the data is represented continuously in a function space and 3D shapes are learned as a continuous decision boundary.
• Training on Polar Image Transformations Improves Biomedical Image Segmentation
  29 Sep 2021 1 repository listed
  We show that our method produces state-of-the-art results for lesion, liver, and polyp segmentation and performs better than most common neural network architectures for biomedical image segmentation.
• Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning
  13 Sep 2021 1 repository listed
  In this work, we report a novel unsupervised domain adaptation framework for cross-modality liver segmentation via joint adversarial learning and self-learning.
• Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
  14 Apr 2021 1 repository listed
  Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can…
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation
  7 Apr 2021 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.
• Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study
  25 Jan 2021 1 repository listed
  Recently, with the development of Deep Learning (DL) algorithms, automatic organ segmentation has been gathered lots of attention from the researchers.
• Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
  20 Nov 2020 1 repository listed
  Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors.
• KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
  4 Oct 2020 1 repository listed
  To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers…
• Liver segmentation and metastases detection in MR images using convolutional neural networks
  15 Oct 2019 1 repository listed
  Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome.
• Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation
  22 Aug 2019 1 repository listed
  In this study, the optimal input configuration of DCE MR images for convolutional neural networks (CNNs) is studied.
• Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations
  23 Jun 2019 1 repository listed
  Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD).
• Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function
  9 May 2019 1 repository listed
  At present, lesion segmentation is still performed manually (or semi-automatically) by medical experts.
• Imperfect Segmentation Labels: How Much Do They Matter?
  12 Jun 2018 1 repository listed
  Labeled datasets for semantic segmentation are imperfect, especially in medical imaging where borders are often subtle or ill-defined.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.