We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models. Our method extends standard segmentation pipelines to focus on higher target recall or reduction of noisy false-positive predictions, boosting overall segmentation performance. To achieve this, we include segmentation errors into a new learning process appended to the main training setup, allowing the model to find features which explain away previous errors. We evaluate this on semantically distinct architectures: cascaded two- and three-dimensional as well as combined learning setups for multitask segmentation. Liver and lesion segmentation data are provided by the Liver Tumor Segmentation challenge (LiTS), with an increase in dice score of up to 2 points.
Heidelberg University（海德堡大学）；University Medical Center Mannheim（曼海姆大学医学中心）
At present, lesion segmentation is still performed manually (or semi-automatically) by medical experts. To facilitate this process, we contribute a fully-automatic lesion segmentation pipeline. This work proposes a method as a part of the LiTS (Liver Tumor Segmentation Challenge) competition for ISBI 17 and MICCAI 17 comparing methods for automatics egmentation of liver lesions in CT scans. By utilizing cascaded, densely connected 2D U-Nets and a Tversky-coefficient based loss function, our framework achieves very good shape extractions with high detection sensitivity, with competitive scores …
The success of supervised lesion segmentation algorithms using Computed Tomography (CT) exams depends significantly on the quantity and variability of samples available for training. While annotating such data constitutes a challenge itself, the variability of lesions in the dataset also depends on the prevalence of different types of lesions. This phenomenon adds an inherent bias to lesion segmentation algorithms that can be diminished, among different possibilities, using aggressive data augmentation methods. In this paper, we present a method for implanting realistic lesions in CT slices to…
Accurate liver and lesion segmentation from computed tomography (CT) images are highly demanded in clinical practice for assisting the diagnosis and assessment of hepatic tumor disease. However, automatic liver and lesion segmentation from contrast-enhanced CT volumes is extremely challenging due to the diversity in contrast, resolution, and quality of images. Previous methods based on UNet for 2D slice-by-slice or 3D volume-by-volume segmentation either lack sufficient spatial contexts or suffer from high GPU computational cost, which limits the performance. To tackle these issues, we propose…
Lesion synthesis received much attention with the rise of efficient generative models for augmenting training data, drawing lesion evolution scenarios, or aiding expert training. The quality and diversity of synthesized data are highly dependent on the annotated data used to train the models, which not rarely struggle to derive very different yet realistic samples from the training ones. That adds an inherent bias to lesion segmentation algorithms and limits synthesizing lesion evolution scenarios efficiently. This paper presents a method for decoupling shape and density for liver lesion synth…
Transfer learning and joint learning approaches are extensively used to improve the performance of Convolutional Neural Networks (CNNs). In medical imaging applications in which the target dataset is typically very small, transfer learning improves feature learning while joint learning has shown effectiveness in improving the network's generalization and robustness. In this work, we study the combination of these two approaches for the problem of liver lesion segmentation and classification. For this purpose, 332 abdominal CT slices containing lesion segmentation and classification of three le…
PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scans
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；DAMO Academy, Alibaba Group（阿里集团达摩院）；Hupan Lab（汇看实验室）；Department of Radiology, Shengjing Hospital of China Medical University（中国医科大学附属盛京医院放射科）；Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院肝胆外科）
Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care. In this paper we propose a cascaded system that combines both 2D and 3D convolutional neural networks to effectively segment hepatic lesions. Our 2D network operates on a slice by slice basis to segment the liver and larger tumors, while we use a 3D network to detect small lesions that are often missed in a 2D segmentation design. We employ this algorithm on the LiTS challenge obtaining a Dice score per …
University of Science and Technology of China（中国科学技术大学）；University of Miami（迈阿密大学）；the First Affiliated Hospital of USTC（中国科学技术大学附属第一医院）
Automatic deep learning segmentation models has been shown to improve both the segmentation efficiency and the accuracy. However, training a robust segmentation model requires considerably large labeled training samples, which may be impractical. This study aimed to develop a deep learning framework for generating synthetic lesions that can be used to enhance network training. The lesion synthesis network is a modified generative adversarial network (GAN). Specifically, we innovated a partial convolution strategy to construct an Unet-like generator. The discriminator is designed using Wasserst…
Eastern Hepatobiliary Surgery Hospital（东方肝胆外科医院）；Ping An Technology（平安科技）；Georgia Institute of Technology（佐治亚理工学院）；PAII Inc.（PAII公司）
Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation. Methods: we enrolled 400 patients who had either liver resection or a biopsy and was diagnosed with either hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma, or secondary metastasis, from 2006 to 2019. Each patient was scanned with T1WI, T2WI, T1WI venous phase (T2WI-V), T1WI arterial phase (T1WI-A), and DWI MRI sequences. We propose a fully-automatic deep CAD pipeline that localizes lesions from 3D MRI studies using key-slice parsing an…
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans
King Fahd University of Petroleum and Minerals（法赫德国王石油矿产大学）；Vinbrain JSC（Vinbrain股份公司）；Le Quy Don Technical University（黎贵敦技术大学）；University Medical Center at Ho Chi Minh City（胡志明市大学医疗中心）
Otto-von-Guericke University（奥托·冯·格里克大学）；University Hospital of Magdeburg（马格德堡大学医院）
Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. T…
We present an automatic method for joint liver lesion segmentation and classification using a hierarchical fine-tuning framework. Our dataset is small, containing 332 2-D CT examinations with lesion annotated into 3 lesion types: cysts, hemangiomas, and metastases. Using a cascaded U-net that performs segmentation and classification simultaneously, we trained a strong lesion segmentation model on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. We used the trained weights to fine-tune a slightly modified model to obtain improved lesion segmentation and classification, on t…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Department of Biomedical Engineering, Eindhoven University of Technology（埃因霍温理工大学生物医学工程系）
Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a technique to reduce false positive detections made by a neural network using an SVM classifier trained with features derived from the uncertainty map of the neural network prediction. We demonstrate the effectiveness of this method for the detection of liver lesions on a dataset of abdominal MR images. We find that the use of a dropout rate of 0.5 produces the least number of false positives in the neural network predicti…
The University of Hong Kong（香港大学）；The University of Hong Kong-Shenzhen Hospital（香港大学深圳医院）
In this work, we propose a new segmentation network by integrating DenseUNet and bidirectional LSTM together with attention mechanism, termed as DA-BDense-UNet. DenseUNet allows learning enough diverse features and enhancing the representative power of networks by regulating the information flow. Bidirectional LSTM is responsible to explore the relationships between the encoded features and the up-sampled features in the encoding and decoding paths. Meanwhile, we introduce attention gates (AG) into DenseUNet to diminish responses of unrelated background regions and magnify responses of salient…
PAII Inc.（PAII公司）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Chang Gung Memorial Hospital（长庚纪念医院）；PingAn Technology（平安科技）
In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of …
SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging
Deepwise Healthcare（深睿医疗）；Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院附属邵逸夫医院）；Ningbo Medical Center Lihuili Hospital（宁波医疗中心李惠利医院）；The University of Hong Kong（香港大学）
T-CACE: A Time-Conditioned Autoregressive Contrast Enhancement Multi-Task Framework for Contrast-Free Liver MRI Synthesis, Segmentation, and Diagnosis
Department of Computer Science, Toronto Metropolitan University（计算机科学系，多伦多 Metropolitan 大学）；School of Biomedical Engineering, Western University（生物医学工程学院，西部大学）
Precise Liver Tumor Segmentation in CT Using a Hybrid Deep Learning-Radiomics Framework
School of Information Science & Engineering, Shandong Normal University（信息科学与工程学院，山东师范大学）；Tajik State University of Law, Business and Politics（塔吉克国立法律、商业与政治大学）
SAS Institute Inc.（SAS研究所公司）；SAS Institute B.V.（SAS研究所私人有限公司）；Amsterdam University Medical Centers（阿姆斯特丹大学医学中心）
Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths worldwide. Most CRC deaths are the result of progression of metastases. The assessment of metastases is done using the RECIST criterion, which is time consuming and subjective, as clinicians need to manually measure anatomical tumor sizes. AI has many successes in image object detection, but often suffers because the models used are not interpretable, leading to issues in trust and implementation in the clinical setting. We propose a framework for an AI-augmented system in which an int…
Department of Electrical and Computer Engineering, Seoul National University（首尔大学电气与计算机工程系）；Department of Radiology, Seoul National University College of Medicine（首尔大学医学院放射科）；Department of Radiology, Seoul National University Hospital（首尔大学医院放射科）；Institute of Radiation Medicine, Seoul National University Medical Research Center（首尔大学医学研究中心放射医学研究所）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔大学人工智能跨学科项目）
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. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multi…
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Ping An Insurance Company of China（中国平安保险公司）
Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment. In this work, we propose a two-stage framework for 2D liver and tumor segmentation. The first stage is a coarse liver segmentation network, while the second stage is an edge enhanced network (E$^2$Net) for more accurate liver and tumor segmentation. E$^2$Net explicitly models complementary objects (liver and tumor) and their edge information within the network to preserve the organ and lesion boundaries. We introduce…
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Shanghai AI Lab（上海人工智能实验室）；University of Liverpool（利物浦大学）；Columbia university（哥伦比亚大学）；Monash University（墨尔本大学）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；MBZUAI（穆斯林人工智能研究所）；People's Hospital of Gong'an County（公安县人民医院）
本文提出首个 3D 全腹部病变多类分割数据集 MSWAL，覆盖七类常见病变，并提出 Inception nnU-Net 框架，在分割性能上显著优于现有方法。
Zhejiang University（浙江大学）；University of Science and Technology of China（中国科学技术大学）；Zhejiang Lab（之江实验室）；Ritsumeikan University（立命馆大学）；Sir Run Run Shaw Hospital（邵逸夫医院）；The First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
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,…
James Cook University（詹姆斯·库克大学）；Mahidol University（玛希隆大学）；University of Bremen（不来梅大学）
Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer vision tasks, including medical image processing. In this technical report, we carry out research focused on more careful approach to the process of learning rather than on complex architecture of the CNN. We have…
University of California, Los Angeles（加州大学洛杉矶分校）；Stanford University（斯坦福大学）
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a fully automated segmentation framework for that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities of Active Contour Models (ACMs). Our DALS framework benefits from an improved level-set ACM formulation with a per-pixel-parameterized energy fun…
Surgical resections are the most prevalent curative treatment for primary liver cancer. Tumors located in critical positions are known to complexify liver resections (LR). While experienced surgeons in specialized medical centers may have the necessary expertise to accurately anticipate LR complexity, and prepare accordingly, an objective method able to reproduce this behavior would have the potential to improve the standard routine of care, and avoid intra- and postoperative complications. In this article, we propose CoRe, an automated medical image processing pipeline for the prediction of p…
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography
Radboud University Medical Center（拉德堡德大学医学中心）；Jeroen Bosch Hospital（杰伦博世医院）；University Medical Center Groningen（格罗宁根大学医学中心）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学梅维斯研究所）
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification
Univ. Grenoble Alpes（格勒诺布尔大学）；Inserm（法国国家健康与医学研究院）；Grenoble Institut Neurosciences（格勒诺布尔神经科学研究所）；Pixyl（Pixyl公司）；Research and Development Laboratory（研发实验室）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔综合理工学院）；LJK（格勒诺布尔大学联合实验室（注：因LJK无明确对应中文通用名称，按原文保留英文缩写））
Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports
Institute of Medical Research, Northwestern Polytechnical University（西北工业大学医学研究院）；School of Computing, University of Georgia（佐治亚大学计算学院）；School of Life Science and Technology, University of Electronic Science and Technology of China（电子科技大学生命科学与技术学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Lingang Laboratory（临港实验室）
Zhejiang University（浙江大学）；Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属邵逸夫医院）；The First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；Ritsumeikan University（立命馆大学）；Yamaguchi University（山口大学）；Zhejiang Lab（之江实验室）
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intra-phase attention (Intra-PA) module and an inter-phase attention (Inter-PA) module to capture channel-wise self-dependencies and cross-phase interdependencies, respectively. Thus it enables the network to learn more representative multi-phase features by re…
ViPSAM: Visual Prompting Medical Image Segmentation Using Segment Anything Model
Department of Artificial Intelligence, Sungkyunkwan University, Republic of Korea（人工智能系，成均馆大学，大韩民国）；Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea（放射肿瘤科，三星医疗中心，成均馆大学医学院，大韩民国）；Department of MetaBioHealth, Sungkyunkwan University, Republic of Korea（元生物健康系，成均馆大学，大韩民国）
Deep Regression 2D-3D Ultrasound Registration for Liver Motion Correction in Focal Tumor Thermal Ablation
Robarts Research Institute（罗伯茨研究所）；Western University（西安大略大学）；School of Biomedical Engineering, Western University（西安大略大学生物医学工程学院）；Lawson Health Research Institute（劳森健康研究所）
This study's objective was to segment spinal metastases in diagnostic MR images using a deep learning-based approach. Segmentation of such lesions can present a pivotal step towards enhanced therapy planning and validation, as well as intervention support during minimally invasive and image-guided surgeries like radiofrequency ablations. For this purpose, we used a U-Net like architecture trained with 40 clinical cases including both, lytic and sclerotic lesion types and various MR sequences. Our proposed method was evaluated with regards to various factors influencing the segmentation quality…
BreastSegNet: Multi-label Segmentation of Breast MRI
Department of Biostatistics \& Bioinformatics, Duke University Department of Electrical；Computer Engineering, Duke University Department of Radiology, Duke University Medical Center Department of Computer Science, Duke University
MedSAM2: Segment Anything in 3D Medical Images and Videos
University Health Network（多伦多大学健康网络）；Vector Institute（向量研究院）；Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；University of Toronto（多伦多大学）；Peter Munk Cardiac Centre（彼得·芒克心脏中心）；AI Hub（人工智能中心）
提出MedSAM2，通过微调Segment Anything Model 2构建3D医学图像和视频分割基础模型，结合人在回路流程显著降低标注成本并提升分割性能。
University of Georgia（佐治亚大学）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）；Shanghai Jiao Tong University（上海交通大学）
In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-level annotations from experts and the existence of a large amount of unannotated normal and abnormal image scans. We introduce a segmentation network that utilizes adversarial learning to partition an image into two cuts, with one of them falling into a reference distribution provided by the user. This Adversarial-based Selective Cutting network (ASC-Net) bridges the two domains of cluster-based deep segmentation and …
