AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images.
AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images.
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AI驱动的3D CT图像上的稳健肾脏和肾脏质量分割和分类。
DOI：
10.3390/bioengineering10010116
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发表时间：
2023-01-13
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--
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肾癌的早期干预有助于提高生存率。腹部计算机断层扫描（CT）常用于诊断肾脏肿块。在临床实践中，人工对器官和肿瘤进行分割和定量是昂贵且耗时的。人工智能（AI）在辅助癌症诊断方面显示出显著优势。为了减少人工分割的工作量，避免不必要的活检或手术，在本文中，我们提出了一个新的端到端人工智能驱动的肾脏和肾脏肿块自动诊断框架，以识别肾脏的异常区域并诊断肾细胞癌（RCC）的组织学亚型。该框架首先通过3D深度学习架构（Res-UNet）对肾脏和肾肿块区域进行分割，然后利用局部和全局特征对最常见的RCC进行亚型预测的双路径分类网络：透明细胞、憎色细胞、嗜瘤细胞、乳头状和其他RCC亚型。为了提高所提框架对来自不同机构的数据集的鲁棒性，提出了一种弱监督学习模式，通过很少的CT切片注释来利用不同供应商之间的领域差距。我们提出的诊断系统可以准确地分割肾脏和肾脏肿块区域并预测肿瘤亚型，优于现有的KiTs19数据集方法。此外，跨数据集验证结果证明了通过弱监督学习模式训练的来自不同机构的数据集的鲁棒性。
Early intervention in kidney cancer helps to improve survival rates. Abdominal computed tomography (CT) is often used to diagnose renal masses. In clinical practice, the manual segmentation and quantification of organs and tumors are expensive and time-consuming. Artificial intelligence (AI) has shown a significant advantage in assisting cancer diagnosis. To reduce the workload of manual segmentation and avoid unnecessary biopsies or surgeries, in this paper, we propose a novel end-to-end AI-driven automatic kidney and renal mass diagnosis framework to identify the abnormal areas of the kidney and diagnose the histological subtypes of renal cell carcinoma (RCC). The proposed framework first segments the kidney and renal mass regions by a 3D deep learning architecture (Res-UNet), followed by a dual-path classification network utilizing local and global features for the subtype prediction of the most common RCCs: clear cell, chromophobe, oncocytoma, papillary, and other RCC subtypes. To improve the robustness of the proposed framework on the dataset collected from various institutions, a weakly supervised learning schema is proposed to leverage the domain gap between various vendors via very few CT slice annotations. Our proposed diagnosis system can accurately segment the kidney and renal mass regions and predict tumor subtypes, outperforming existing methods on the KiTs19 dataset. Furthermore, cross-dataset validation results demonstrate the robustness of datasets collected from different institutions trained via the weakly supervised learning schema.
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影响因子：
10.9
作者：
Bilic, Patrick;Christ, Patrick;Li, Hongwei Bran;Vorontsov, Eugene;Ben-Cohen, Avi;Kaissis, Georgios;Szeskin, Adi;Jacobs, Colin;Mamani, Gabriel Efrain Humpire;Chartrand, Gabriel;Lohoefer, Fabian;Holch, Julian Walter;Sommer, Wieland;Hofmann, Felix;Hostettler, Alexandre;Lev-Cohain, Naama;Drozdzal, Michal;Amitai, Michal Marianne;Vivanti, Refael;Sosna, Jacob;Ezhov, Ivan;Sekuboyina, Anjany;Navarro, Fernando;Kofler, Florian;Paetzold, Johannes C.;Shit, Suprosanna;Hu, Xiaobin;Lipkova, Jana;Rempfler, Markus;Piraud, Marie;Kirschke, Jan;Wiestler, Benedikt;Zhang, Zhiheng;Huelsemeyer, Christian;Beetz, Marcel;Ettlinger, Florian;Antonelli, Michela;Bae, Woong;Bellver, Miriam;Bi, Lei;Chen, Hao;Chlebus, Grzegorz;Dam, Erik B.;Dou, Qi;Fu, Chi-Wing;Georgescu, Bogdan;Giro-I-Nieto, Xavier;Gruen, Felix;Han, Xu;Heng, Pheng-Ann;Hesser, Jurgen;Moltz, Jan Hendrik;Igel, Christian;Isensee, Fabian;Jaeger, Paul;Jia, Fucang;Kaluva, Krishna Chaitanya;Khened, Mahendra;Kim, Ildoo;Kim, Jae-Hun;Kim, Sungwoong;Kohl, Simon;Konopczynski, Tomasz;Kori, Avinash;Krishnamurthi, Ganapathy;Li, Fan;Li, Hongchao;Li, Junbo;Li, Xiaomeng;Lowengrub, John;Ma, Jun;Maier-Hein, Klaus;Maninis, Kevis-Kokitsi;Meine, Hans;Merhof, Dorit;Pai, Akshay;Perslev, Mathias;Petersen, Jens;Pont-Tuset, Jordi;Qi, Jin;Qi, Xiaojuan;Rippel, Oliver;Roth, Karsten;Sarasua, Ignacio;Schenk, Andrea;Shen, Zengming;Torres, Jordi;Wachinger, Christian;Wang, Chunliang;Weninger, Leon;Wu, Jianrong;Xu, Daguang;Yang, Xiaoping;Yu, Simon Chun-Ho;Yuan, Yading;Yue, Miao;Zhang, Liping;Cardoso, Jorge;Bakas, Spyridon;Braren, Rickmer;Heinemann, Volker;Pal, Christopher;Tang, An;Kadoury, Samuel;Soler, Luc;van Ginneken, Bram;Greenspan, Hayit;Joskowicz, Leo;Menze, Bjoern
通讯作者：
Menze, Bjoern
DOI：
10.3390/diagnostics12081788
发表时间：
2022-07-23
期刊：
Diagnostics (Basel, Switzerland)
影响因子：
--
作者：
通讯作者：
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影响因子：
4.4
作者：
Han, Seokmin;Hwang, Sung Il;Lee, Hak Jong
通讯作者：
Lee, Hak Jong
影响因子：
23.4
作者：
Capitanio U;Bensalah K;Bex A;Boorjian SA;Bray F;Coleman J;Gore JL;Sun M;Wood C;Russo P
通讯作者：
Russo P
DOI：
10.1038/nrdp.2017.9
发表时间：
2017-03-09
期刊：
Nature reviews. Disease primers
影响因子：
--
作者：
Hsieh JJ;Purdue MP;Signoretti S;Swanton C;Albiges L;Schmidinger M;Heng DY;Larkin J;Ficarra V
通讯作者：
Ficarra V