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De la Cruz-Ku G, Mallouh MP, Torres Roman JS, Linshaw D. Three-dimensional virtual reality in surgical planning for breast cancer with reconstruction. SAGE Open Med Case Rep 2023; 11:2050313X231179299. [PMID: 37325162 PMCID: PMC10262605 DOI: 10.1177/2050313x231179299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Breast surgery is performed to achieve local control in patients with breast cancer. Visualization of the anatomy with a virtual reality software platform reconstructed from magnetic resonance imaging data improves surgical planning with regards to volume and localization of the tumor, lymph nodes, blood vessels, and surrounding tissue to perform oncoplastic tissue rearrangement. We report the use and advantages of virtual reality added to the magnetic resonance imaging assessment in a 36-year-old woman with breast cancer who underwent nipple sparing mastectomy with tissue expander reconstruction.
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Affiliation(s)
- Gabriel De la Cruz-Ku
- Department of Surgery, University of Massachusetts Medical School, Worcester, MA, USA
- Universidad Científica del Sur, Lima, Perú
| | - Michael P Mallouh
- Department of Surgery, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jr Smith Torres Roman
- South American Center for Education and Research in Public Health, Universidad Norbert Wiener, Lima, Perú
| | - David Linshaw
- Department of Surgery, University of Massachusetts Medical School, Worcester, MA, USA
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Bindlish A, Sawal A. A Detailed Description and Discussion on Conjoined Twins. Cureus 2022; 14:e29526. [PMID: 36312620 PMCID: PMC9595239 DOI: 10.7759/cureus.29526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/24/2022] [Indexed: 11/18/2022] Open
Abstract
Conjoined twins are described as having been physically fused during pregnancy and delivery. They were first mentioned a long time ago when there was not much known about this. They share some organs that are vital for survival, like the heart; these twins are almost impossible to save, but there are some cases wherein there is evidence of their survival. The article aims to present a unique discussion on conjoined twins. This article talks about the formation of conjoint twins, their types and nomenclatures, embryological concepts, past history/traditional tales, case studies, and the medical enhancements happening in this area. Both fission and fusion are thought to contribute to the disease. A monozygotic twin pregnancy cleaves when it occurs more than thirteen days after fertilization. There is just one placenta and one womb for conjoined twins (one amniotic sac). The twins that are born and stay alive after delivery usually stay alive for a few days or weeks. It’s pretty rare for them to live a long prosperous life, but this article shows the otherwise, too, like the Siamese twins, which is a unique example of conjoint twins who lived for a long time. This kind of pregnancy is a complex procedure that needs to be managed by a team of professionals.
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Guérinot C, Marcon V, Godard C, Blanc T, Verdier H, Planchon G, Raimondi F, Boddaert N, Alonso M, Sailor K, Lledo PM, Hajj B, El Beheiry M, Masson JB. New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing. FRONTIERS IN BIOINFORMATICS 2022; 1:777101. [PMID: 36303792 PMCID: PMC9580868 DOI: 10.3389/fbinf.2021.777101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/15/2021] [Indexed: 01/02/2023] Open
Abstract
Three-dimensional imaging is at the core of medical imaging and is becoming a standard in biological research. As a result, there is an increasing need to visualize, analyze and interact with data in a natural three-dimensional context. By combining stereoscopy and motion tracking, commercial virtual reality (VR) headsets provide a solution to this critical visualization challenge by allowing users to view volumetric image stacks in a highly intuitive fashion. While optimizing the visualization and interaction process in VR remains an active topic, one of the most pressing issue is how to utilize VR for annotation and analysis of data. Annotating data is often a required step for training machine learning algorithms. For example, enhancing the ability to annotate complex three-dimensional data in biological research as newly acquired data may come in limited quantities. Similarly, medical data annotation is often time-consuming and requires expert knowledge to identify structures of interest correctly. Moreover, simultaneous data analysis and visualization in VR is computationally demanding. Here, we introduce a new procedure to visualize, interact, annotate and analyze data by combining VR with cloud computing. VR is leveraged to provide natural interactions with volumetric representations of experimental imaging data. In parallel, cloud computing performs costly computations to accelerate the data annotation with minimal input required from the user. We demonstrate multiple proof-of-concept applications of our approach on volumetric fluorescent microscopy images of mouse neurons and tumor or organ annotations in medical images.
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Affiliation(s)
- Corentin Guérinot
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
- Perception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France
- Sorbonne Université, Collège Doctoral, Paris, France
| | - Valentin Marcon
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
| | - Charlotte Godard
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
- École Doctorale Physique en Île-de-France, PSL University, Paris, France
| | - Thomas Blanc
- Sorbonne Université, Collège Doctoral, Paris, France
- Laboratoire Physico-Chimie, Institut Curie, PSL Research University, CNRS UMR168, Paris, France
| | - Hippolyte Verdier
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
- Histopathology and Bio-Imaging Group, Sanofi R&D, Vitry-Sur-Seine, France
- Université de Paris, UFR de Physique, Paris, France
| | - Guillaume Planchon
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
| | - Francesca Raimondi
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
- Unité Médicochirurgicale de Cardiologie Congénitale et Pédiatrique, Centre de Référence des Malformations Cardiaques Congénitales Complexes M3C, Hôpital Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
- Pediatric Radiology Unit, Hôpital Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
- UMR-1163 Institut Imagine, Hôpital Universitaire Necker-Enfants Malades, AP-HP, Paris, France
| | - Nathalie Boddaert
- Pediatric Radiology Unit, Hôpital Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
- UMR-1163 Institut Imagine, Hôpital Universitaire Necker-Enfants Malades, AP-HP, Paris, France
| | - Mariana Alonso
- Perception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France
| | - Kurt Sailor
- Perception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France
| | - Pierre-Marie Lledo
- Perception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France
| | - Bassam Hajj
- Sorbonne Université, Collège Doctoral, Paris, France
- École Doctorale Physique en Île-de-France, PSL University, Paris, France
| | - Mohamed El Beheiry
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
| | - Jean-Baptiste Masson
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
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