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Liu H, Xu Z, Gao R, Li H, Wang J, Chabin G, Oguz I, Grbic S. COSST: Multi-Organ Segmentation With Partially Labeled Datasets Using Comprehensive Supervisions and Self-Training. IEEE Trans Med Imaging 2024; 43:1995-2009. [PMID: 38224508 DOI: 10.1109/tmi.2024.3354673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e., only a subset of organs are annotated. Therefore, it is crucial to investigate how to learn a unified model on the available partially labeled datasets to leverage their synergistic potential. In this paper, we systematically investigate the partial-label segmentation problem with theoretical and empirical analyses on the prior techniques. We revisit the problem from a perspective of partial label supervision signals and identify two signals derived from ground truth and one from pseudo labels. We propose a novel two-stage framework termed COSST, which effectively and efficiently integrates comprehensive supervision signals with self-training. Concretely, we first train an initial unified model using two ground truth-based signals and then iteratively incorporate the pseudo label signal to the initial model using self-training. To mitigate performance degradation caused by unreliable pseudo labels, we assess the reliability of pseudo labels via outlier detection in latent space and exclude the most unreliable pseudo labels from each self-training iteration. Extensive experiments are conducted on one public and three private partial-label segmentation tasks over 12 CT datasets. Experimental results show that our proposed COSST achieves significant improvement over the baseline method, i.e., individual networks trained on each partially labeled dataset. Compared to the state-of-the-art partial-label segmentation methods, COSST demonstrates consistent superior performance on various segmentation tasks and with different training data sizes.
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Pereira M, Pinto J, Arteaga B, Guerra A, Jorge RN, Monteiro FJ, Salgado CL. A Comprehensive Look at In Vitro Angiogenesis Image Analysis Software. Int J Mol Sci 2023; 24:17625. [PMID: 38139453 PMCID: PMC10743557 DOI: 10.3390/ijms242417625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/11/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
One of the complex challenges faced presently by tissue engineering (TE) is the development of vascularized constructs that accurately mimic the extracellular matrix (ECM) of native tissue in which they are inserted to promote vessel growth and, consequently, wound healing and tissue regeneration. TE technique is characterized by several stages, starting from the choice of cell culture and the more appropriate scaffold material that can adequately support and supply them with the necessary biological cues for microvessel development. The next step is to analyze the attained microvasculature, which is reliant on the available labeling and microscopy techniques to visualize the network, as well as metrics employed to characterize it. These are usually attained with the use of software, which has been cited in several works, although no clear standard procedure has been observed to promote the reproduction of the cell response analysis. The present review analyzes not only the various steps previously described in terms of the current standards for evaluation, but also surveys some of the available metrics and software used to quantify networks, along with the detection of analysis limitations and future improvements that could lead to considerable progress for angiogenesis evaluation and application in TE research.
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Affiliation(s)
- Mariana Pereira
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (M.P.); (J.P.); (B.A.); (F.J.M.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
| | - Jéssica Pinto
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (M.P.); (J.P.); (B.A.); (F.J.M.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
| | - Belén Arteaga
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (M.P.); (J.P.); (B.A.); (F.J.M.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
- Faculty of Medicine, University of Granada, Parque Tecnológico de la Salud, Av. de la Investigación 11, 18016 Granada, Spain
| | - Ana Guerra
- INEGI—Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, 4200-465 Porto, Portugal; (A.G.); (R.N.J.)
| | - Renato Natal Jorge
- INEGI—Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, 4200-465 Porto, Portugal; (A.G.); (R.N.J.)
- LAETA—Laboratório Associado de Energia, Transportes e Aeronáutica, Universidade do Porto, 4200-165 Porto, Portugal
- FEUP—Faculdade de Engenharia, Departamento de Engenharia Metalúrgica e de Materiais, Universidade do Porto, 4200-165 Porto, Portugal
| | - Fernando Jorge Monteiro
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (M.P.); (J.P.); (B.A.); (F.J.M.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
- FEUP—Faculdade de Engenharia, Departamento de Engenharia Metalúrgica e de Materiais, Universidade do Porto, 4200-165 Porto, Portugal
- PCCC—Porto Comprehensive Cancer Center, 4200-072 Porto, Portugal
| | - Christiane Laranjo Salgado
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (M.P.); (J.P.); (B.A.); (F.J.M.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
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Timakova A, Ananev V, Fayzullin A, Makarov V, Ivanova E, Shekhter A, Timashev P. Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology. Biomolecules 2023; 13:1327. [PMID: 37759727 PMCID: PMC10526383 DOI: 10.3390/biom13091327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/23/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.
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Affiliation(s)
- Anna Timakova
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
| | - Vladislav Ananev
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 41 B. St. Petersburgskaya, 173003 Veliky Novgorod, Russia; (V.A.); (V.M.)
| | - Alexey Fayzullin
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
| | - Vladimir Makarov
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 41 B. St. Petersburgskaya, 173003 Veliky Novgorod, Russia; (V.A.); (V.M.)
| | - Elena Ivanova
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
- B.V. Petrovsky Russian Research Center of Surgery, 2 Abrikosovskiy Lane, 119991 Moscow, Russia
| | - Anatoly Shekhter
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
| | - Peter Timashev
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia
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