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    • Submucosal Fibroids
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    • Mousumi Bhaduri: Influence Statistics

      Mousumi Bhaduri

      Mousumi Bhaduri

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      Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada | London Health Sciences Centre, London, ON, Canada | London Health Sciences Center (LHSC), ...

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      Mousumi Bhaduri:Expert Impact

      Concepts for whichMousumi Bhadurihas direct influence:Submucosal fibroids,Direct estimation,Cardiac indices,Cardiac images,Volume estimation,Regression forests,Multioutput regression,Sparse latent regression.

      Mousumi Bhaduri:KOL impact

      Concepts related to the work of other authors for whichfor which Mousumi Bhaduri has influence:Deep learning,Endometrial polyps,Ventricle segmentation,Direct estimation,Nasal bones,Abnormal uterine bleeding,Cecal bascule.

      KOL Resume for Mousumi Bhaduri

      Year
      2017

      Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada

      2016

      London Health Sciences Center (LHSC), London, ON, Canada

      2015

      London Health Sciences Ctr. (Canada)

      2014

      Department of Medical Imaging, Schulich School of Medicine and Dentistry, University of Western Ontario, London Health Sciences Center‐Victoria Hospital London, Ontario, Canada

      2013

      Department of Radiology, Western University, London, Ontario, Canada

      2012

      Department of Medical Imaging, Schulich School of Medicine & Dentistry, University of Western Ontario, London Health Sciences Centre, Victoria Hospital, 800 Commissioners Rd East, Rm D1-106, London, ON N6A 5W9, Canada.

      2010

      Department of Medical Imaging, Mount Sinai Hospital, University of Toronto, Toronto, Canada

      2009

      Department of Medical Imaging , University of Toronto, Toronto, Ontario, Canada

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      Sample of concepts for which Mousumi Bhaduri is among the top experts in the world.
      Concept World rank
      combination sonohysterographic features #4
      sonographic findings lrs #4
      combination sonohysterographic #4
      sonographic features sonohysterography #4
      sonohysterographic features #4
      lrs endometrial polyps #4
      discriminating polyps #4
      method multitype #4
      incompatible regression model #4
      wall thicknesses areas #4
      cardiac volumes estimation #4
      polyps submucosal fibroids #4
      direct multitype #4
      vascular pattern reader #4
      lrs discriminating #4
      estimation multitype #4
      200 sonograms #4
      features sonohysterography #4
      submucosal fibroids combination #4
      lrs polyps #4
      cardiac indices estimation #4
      sonohysterography features #4
      tightlycoupled networks #4
      fibroids combination #4
      correlations mtsr #5
      inputoutput relationships paper #5
      leiomyoma likelihood #5
      regression task mtsr #5
      multitask shape regression #5
      mtsr achieves #5
      cardiac image representation #5
      echogenicity highest #5
      shapes capturing #5
      strength multiple tasks #5
      mtsr jointly #5
      task mtsr #5
      task correlations mtsr #5
      longdesired general framework #5
      regression mtsr #5
      regularization sdl #6
      unsupervised freeview #6
      intertarget correlations #6
      chamber volume estimation #6
      groupwise cardiac #6
      ssnbased groupwise analysis #6
      complexity chambers #6
      synchronized superpixels #6
      interference chambers #6
      regulated modality #6
      multioutput regression sdl #6
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      Prominent publications by Mousumi Bhaduri

      KOL-Index: 7640

      Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and the complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression ...

      Known for Cardiac Indices | Regression Learning | Neural Networks | Joint Representation | Image Interpretation
      KOL-Index: 5681

      Cardiac four-chamber volumes provide crucial information for quantitative analysis of whole heart functions. Conventional cardiac volume estimation relies on a segmentation step; recently emerging direct estimation without segmentation has shown better performance than than segmentation-based methods. However, due to the high complexity, four-chamber volume estimation poses great challenges to these existing methods: four-chamber segmentation is not feasible due to intensity homogeneity ...

      Known for Volume Estimation | Multioutput Regression | Existing Methods | Correlation Coefficient | Discriminative Features
      KOL-Index: 5453

      Accurate estimation of ventricular volumes plays an essential role in clinical diagnosis of cardiac diseases. Existing methods either rely on segmentation or are restricted to direct estimation of the left ventricle. In this paper, we propose a novel method for direct and joint volume estimation of bi-ventricles, i.e., the left and right ventricles, without segmentation and user inputs. Based on the cardiac image representation by multiple and complementary features, we adopt regression ...

      Known for Direct Estimation | Regression Forests | Ventricular Volumes | Computerassisted Imaging | Cardiac Diseases
      KOL-Index: 5221

      Direct estimation of cardiac ventricular volumes has become increasingly popular and important in cardiac function analysis due to its effectiveness and efficiency by avoiding an intermediate segmentation step. However, existing methods rely on either intensive user inputs or problematic assumptions. To realize the full capacities of direct estimation, this paper presents a general, fully learning-based framework for direct bi-ventricular volume estimation, which removes user inputs and ...

      Known for Volume Estimation | Computerassisted Imaging | Networks Computer | Existing Methods | Cardiac Image
      KOL-Index: 3909

      OBJECTIVES: Transcutaneous bowel sonography is a nonionizing imaging modality used in inflammatory bowel disease. Although available in Europe, its uptake in North America has been limited. Since the accuracy of bowel sonography is highly operator dependent, low-volume centers in North America may not achieve the same diagnostic accuracy reported in the European literature. Our objective was to determine the diagnostic accuracy of bowel sonography in a nonexpert low-volume ...

      Known for Inflammatory Bowel Disease | Sensitivity Specificity | Ultrasonography Adult | Ulcerative Colitis | Small Bowel
      KOL-Index: 3763

      Image-based diagnosis and population study on cardiac problems require automatic segmentation on increasingly large amount of data from different protocols, different views, and different patients. However, current algorithms are often limited to regulated settings such as fixed view and single image from one specific modality, where the supervised learning methods can be easily employed but with restricted usability. In this paper, we propose the unsupervised free-view groupwise M3 ...

      Known for Cardiac Images | Synchronized Spectral Network | Groupwise Segmentation | Increasingly Large | Modality Chamber
      KOL-Index: 3213

      OBJECTIVE: To determine the visualization rates of fetal anatomic structures by three-dimensional ultrasound (3DUS) at 12-13 weeks of gestation.

      STUDY DESIGN: This was a prospective observational study of women presenting for nuchal translucency ultrasound. Five 3D volumes of the fetus were acquired transabdominally. Two investigators independently reviewed the stored volumes offline following a standardized protocol.

      RESULTS: One hundred singleton fetuses were examined. The mean time ...

      Known for Three‐dimensional Ultrasound | Nuchal Translucency | Fetal Anatomic | Pregnancy Trimester | Ultrasonography Prenatal
      KOL-Index: 3131

      In this paper, we propose a general segmentation framework of Multi-Task Shape Regression (MTSR) which formulates segmentation as multi-task learning to leverage its strength of jointly solving multiple tasks enhanced by capturing task correlations. The MTSR entirely estimates coordinates of all points on shape contours by multi-task regression, where estimation of each coordinate corresponds to a regression task; the MTSR can jointly handle nonlinear relationships between image ...

      Known for Shape Regression | Medical Image Segmentation | Coordinate Correlations | Nonlinear Relationships | Image Appearance
      KOL-Index: 3120

      Cardiac four-chamber volume estimation serves as a fundamental and crucial role in clinical quantitative analysis of whole heart functions. It is a challenging task due to the huge complexity of the four chambers including great appearance variations, huge shape deformation and interference between chambers. Direct estimation has recently emerged as an effective and convenient tool for cardiac ventricular volume estimation. However, existing direct estimation methods were specifically ...

      Known for Direct Estimation | Challenging Task | Heart Ventricles | Correlation Coefficient | Machine Learning
      KOL-Index: 3016

      Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which ...

      Known for Descriptor Learning | Manifold Regularization | Computer Vision | Discriminative Features | Low Dimensional
      KOL-Index: 1957

      The diagnosis, comparative and population study of cardiac radiology data require heart segmentation on increasingly large amount of images from different modalities/chambers/patients under various imaging views. Most existing automatic cardiac segmentation methods are often limited to single image segmentation with regulated modality/region settings or well-cropped ROI areas, which is impossible for large datasets due to enormous device protocols and institutional differences. A pure ...

      Known for Spectral Network | Medical Images | Computed Algorithms | Segmentation Methods
      KOL-Index: 1904

      OBJECTIVE: The objective of our study was to determine whether applying specific diagnostic criteria to the interpretation of sonohysterography would improve the diagnostic accuracy of the interpretation.

      MATERIALS AND METHODS: This retrospective study included 200 consecutive patients who underwent both sonohysterography and a procedure that resulted in a positive pathologic diagnosis. The initial interpretation (reader 1) was performed at the time of the examination. Subsequently, a ...

      Known for Pathologic Diagnosis | Submucosal Fibroid | 200 Consecutive Patients | 80 Biopsy | Endometrial Hyperplasia
      KOL-Index: 1729

      OBJECTIVES: The purpose of this study was to determine which combination of sonohysterographic features has the highest likelihood ratios (LRs) in discriminating polyps from submucosal fibroids.

      METHODS: This retrospective study included 200 consecutive patients who underwent both sonohysterography and a procedure resulting in a positive pathologic diagnosis. A reader, masked to the imaging and pathologic findings, independently reviewed the 200 sonograms and recorded the findings using ...

      Known for Endometrial Polyps | Algorithms Diagnosis | Single Vessel
      KOL-Index: 1700

      Multitarget regression has recently generated intensive popularity due to its ability to simultaneously solve multiple regression tasks with improved performance, while great challenges stem from jointly exploring inter-target correlations and input-output relationships. In this paper, we propose multitarget sparse latent regression (MSLR) to simultaneously model intrinsic intertarget correlations and complex nonlinear input-output relationships in one single framework. By deploying a ...

      Known for Multitarget Regression | Intertarget Correlations | Great Effectiveness | Input Output | Framework Deploying
      KOL-Index: 379
      Known for Volume Estimation

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      Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada | London Health Sciences Centre, London, ON, Canada | London Health Sciences Center (LHSC), London, ON, Canada | London Healthcare Sciences Centre, London, ON, Canada | Lo

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