Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
Vanderbilt University（范德堡大学）；Sigma Technologies（12西格玛科技公司）；Vanderbilt University Medical Center（范德堡大学医学中心）
Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiological assessment. A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, continued cross-validation on open datasets presents the risk of indirect knowledge contamination and could result in circular reasoning. Moreover, 'real world' segmentations can be challenging due to the wide variability of abdomen physiology within patients. Herein, we perform two data retrievals to capture clinically acquired deidentified abdominal CT cohorts with respect to a recently published variation on 3D U-Net (baseline algorithm). First, we retrieved 2004 deidentified studies on 476 patients with diagnosis codes involving spleen abnormalities (cohort A). Second, we retrieved 4313 deidentified studies on 1754 patients without diagnosis codes involving spleen abnormalities (cohort B). We perform prospective evaluation of the existing algorithm on both cohorts, yielding 13% and 8% failure rate, respectively. Then, we identified 51 subjects in cohort A with segmentation failures and manually corrected the liver and gallbladder labels. We re-trained the model adding the manual labels, resulting in performance improvement of 9% and 6% failure rate for the A and B cohorts, respectively. In summary, the performance of the baseline on the prospective cohorts was similar to that on previously published datasets. Moreover, adding data from the first cohort substantively improved performance when evaluated on the second withheld validation cohort.
Centrum Wiskunde & Informatica（荷兰国家数学与计算机科学研究所）；Xomnia B.V.（Xomnia公司）；Erasmus University Medical Center（伊拉斯谟大学医学中心）；Delft University of Technology（代尔夫特理工大学）；Leiden University Medical Center（莱顿大学医学中心）
Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations. We employ simple heuristics for automatic data cleaning to minimize data inhomogeneity and label noise. Further, we develop a semi-supervised learning approach utilizing a teacher-student set…
University of Electronic Science and Technology of China（电子科技大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital and Institute（四川省肿瘤医院（四川省肿瘤研究所））；Sichuan Cancer Center（四川省癌症中心）；Sichuan University（四川大学）；West China Hospital（华西医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；SenseTime Research（商汤科技研究院）；Rutgers University（罗格斯大学）
Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from 3D volumes is time-consuming and very expensive. 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, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. In this work, we establish a new large-s…
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT). High intra- and inter-user variability makes this a particularly difficult task for esophageal cancer. This motivates automated solutions, which is the aim of our work. Because CTV delineation is highly context-dependent--it must encompass the GTV and regional lymph nodes (LNs) while also avoiding excessive exposure to the organs at risk (OARs)--we formulate it as a…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Wake Forest School of Medicine（维克森林大学医学院）
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised of patients (n=1,399) referred for MRI treatment-planning and gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) were preprocessed and input to the proposed deep learning workflow fo…
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Osaka University Graduate School of Medicine（大阪大学医学研究生院）
We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891 +/- 0.016 (mean +/- std) and an average…
PAII Inc.（PAII公司）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Huadong Hospital Affiliated to Fudan University（复旦大学附属华东医院）；First Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第一附属医院）；People's Hospital of Shanxi Province（山西省人民医院）；Nanfang Hospital, Southern Medical University（南方医科大学南方医院）；The First Hospital of Lanzhou University（兰州大学第一医院）；Chang Gung University（长庚大学）
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable prohibitive labor-cost of this task, most institutions choose a substantially simplified protocol by delineating a smaller subset of OARs and neglecting the dose distributions associated with other OARs. In this work we propose a novel, automated and highly effective stratified OAR segmentation (SOARS) system using deep learning to precisely delineate a comprehe…
University of Oxford（牛津大学）；Imperial College London（帝国理工学院）
Endoscopy is a widely used imaging modality to diagnose and treat diseases in hollow organs as for example the gastrointestinal tract, the kidney and the liver. However, due to varied modalities and use of different imaging protocols at various clinical centers impose significant challenges when generalising deep learning models. Moreover, the assembly of large datasets from different clinical centers can introduce a huge label bias that renders any learnt model unusable. Also, when using new modality or presence of images with rare patterns, a bulk amount of similar image data and their corre…
Over the past few years, different computer-aided diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to design them. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 22 features. The dataset consists of 1,373 patients, 1,641 skin lesions, and 2,298 images for six different diagnostics: three skin diseases and three skin cancers. …
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；Sichuan Cancer Hospital（四川省肿瘤医院）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
Armed Forces Yangju Hospital（杨州武装部队医院）；Seoul National University（首尔国立大学）
For more clinical applications of deep learning models for medical image segmentation, high demands on labeled data and computational resources must be addressed. This study proposes a coarse-to-fine framework with two teacher models and a student model that combines knowledge distillation and cross teaching, a consistency regularization based on pseudo-labels, for efficient semi-supervised learning. The proposed method is demonstrated on the abdominal multi-organ segmentation task in CT images under the MICCAI FLARE 2022 challenge, with mean Dice scores of 0.8429 and 0.8520 in the validation …
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registra…
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
Radiology and Imaging Sciences（放射学与影像科学）；National Institutes of Health Clinical Center（国家卫生研究院临床中心）；Walter Reed National Military Medical Center（沃尔特·里德国家军事医学中心）；Biostatistics and Clinical Epidemiology Services（生物统计与临床流行病学服务）
Segment Anything Model (SAM) for Radiation Oncology
Mayo Clinic（梅奥诊所）；University of Georgia（佐治亚大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
The University of Hong Kong（香港大学）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）深圳大数据研究院）；Longgang Central Hospital of Shenzhen（深圳龙岗中心医院）；Longgang People Hospital of Shenzhen（深圳龙岗人民医院）
Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To miti…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the microscopic spread of tumor cells, which cannot be visualized in typical clinical images such as computed tomography or magnetic resonance imaging. In current clinical practice, physicians segment CTVs manually based on their relationship with nearby organs and other clinical information, per clinical guidelines. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has been a…
COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for se…
