@conference {Klosa2022a, title = {Tissue segmentation for workflow recognition in open inguinal hernia repair training}, booktitle = {SPIE Medical Imaging}, year = {2022}, month = {02/2022}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego}, abstract = {PURPOSE: As medical education adopts a competency-based training method, experts are spending substantial amounts of time instructing and assessing trainees{\textquoteright} competence. In this study, we look to develop a computer-assisted training platform that can provide instruction and assessment of open inguinal hernia repairs without needing an expert observer. We recognize workflow tasks based on the tool-tissue interactions, suggesting that we first need a method to identify tissues. This study aims to train a neural network in identifying tissues in a low-cost phantom as we work towards identifying the tool-tissue interactions needed for task recognition. METHODS: Eight simulated tissues were segmented throughout five videos from experienced surgeons who performed open inguinal hernia repairs on phantoms. A U-Net was trained using leave-one-user-out cross validation. The average F-score, false positive rate and false negative rate were calculated for each tissue to evaluate the U-Net{\textquoteright}s performance. RESULTS: Higher F-scores and lower false negative and positive rates were recorded for the skin, hernia sac, spermatic cord, and nerves, while slightly lower metrics were recorded for the subcutaneous tissue, Scarpa{\textquoteright}s fascia, external oblique aponeurosis and superficial epigastric vessels. CONCLUSION: The U-Net performed better in recognizing tissues that were relatively larger in size and more prevalent, while struggling to recognize smaller tissues only briefly visible. Since workflow recognition does not require perfect segmentation, we believe our U-Net is sufficient in recognizing the tissues of an inguinal hernia repair phantom. Future studies will explore combining our segmentation U-Net with tool detection as we work towards workflow recognition.}, keywords = {open inguinal hernia repair, segmentation, surgical training, U-Net, workflow recognition}, author = {Elizabeth Klosa and R. Hisey and Tahmina Nazari and Theo Wiggers and Boris Zevin and Tamas Ungi and Gabor Fichtinger} } @article {Pinter2019, title = {Polymorph Segmentation Representation for Medical Image Computing}, journal = {Computer Methods and Programs in Biomedicine}, volume = {171}, year = {2019}, month = {02/2019}, pages = {19-26}, abstract = {

Background and Objective: Segmentation is a ubiquitous operation in medical image computing. Various data representations can describe segmentation results, such as labelmap volumes or surface models. Conversions between them are often required, which typically include complex data processing steps. We identified four challenges related to managing multiple representations: conversion method selection, data provenance, data consistency, and coherence of in-memory objects. Methods: A complex data container preserves identity and provenance of the contained representations and ensures data coherence. Conversions are executed automatically on-demand. A graph containing the implemented conversion algorithms determines each execution, ensuring consistency between various representations. The design and implementation of a software library are proposed, in order to provide a readily usable software tool to manage segmentation data in multiple data representations. A low-level core library called PolySeg implemented in The Visualization Toolkit (VTK) manages the data objects and conversions. It is used by a high-level application layer, which has been implemented in the medical image visualization and analysis platform 3D Slicer. The application layer provides advanced visualization, transformation, interoperability, and other functions. Results: The core conversion algorithms comprising the graph were validated. Several applications were implemented based on the library, demonstrating advantages in terms of usability and ease of software development in each case. The Segment Editor application provides fast, comprehensive, and easy-to-use manual and semi-automatic segmentation workflows. Clinical applications for gel dosimetry, external beam planning, and MRI-ultrasound image fusion in brachytherapy were rapidly prototyped resulting robust applications that are already in use in clinical research. The conversion algorithms were found to be accurate and reliable using these applications. Conclusions:\  A generic software library has been designed and developed for automatic management of multiple data formats in segmentation tasks. It enhances both user and developer experience, enabling fast and convenient manual workflows and quicker and more robust software prototyping. The software{\textquoteright}s BSD-style open-source license allows complete freedom of use of the library.

}, keywords = {3D Slicer, DICOM, Open-source, segmentation, Software library, Voxelization}, doi = {https://doi.org/10.1016/j.cmpb.2019.02.011}, author = {Csaba Pinter and Andras Lasso and Gabor Fichtinger} } @conference {Moult2012a, title = {Implicit active contours for automatic brachytherapy seed segmentation in fluoroscopy}, booktitle = {SPIE Medical Imaging 2012}, year = {2012}, month = {02/2012}, pages = {831611}, address = {San Diego, California, USA}, keywords = {Active contours, Intra-operative dosimetry, Prostate brachytherapy, Seed declustering, segmentation}, doi = {http://dx.doi.org/10.1117/12.911153}, author = {Eric Moult and E. Clif Burdette and Danny Y Song and Gabor Fichtinger and Pascal Fallavollita} } @article {Moult2012b, title = {Segmentation of iodine brachytherapy implants in fluoroscopy}, journal = {International Journal of Computer Assisted Radiology and Surgery}, year = {2012}, keywords = {Active contours, Iodine implants, Prostate brachytherapy, Seed declustering, segmentation}, doi = {10.1007/s11548-012-0679-4}, url = {http://www.springerlink.com/content/kmn31823g053wj55/}, author = {Eric Moult and Gabor Fichtinger and William J. Morris and Septimiu E. Salcudean and Ehsan Dehghan and Pascal Fallavollita} } @conference {Avni2010, title = {Design of a predictive targeting error simulator for MRI-guided prostate biopsy}, booktitle = {Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling}, year = {2010}, month = {2010}, pages = {76251A-1 - 76251A-8}, publisher = {SPIE}, organization = {SPIE}, address = {San Diego, California, USA}, abstract = {

Multi-parametric MRI is a new imaging modality superior in quality to Ultrasound (US) which is currently used in standard prostate biopsy procedures. Surface-based registration of the pre-operative and intra-operative prostate volumes is a simple alternative to side-step the challenges involved with deformable registration. However, segmentation errors inevitably introduced during prostate contouring spoil the registration and biopsy targeting accuracies. For the crucial purpose of validating this procedure, we introduce a fully interactive and customizable simulator which determines the resulting targeting errors of simulated registrations between prostate volumes given user-provided parameters for organ deformation, segmentation, and targeting. We present the workflow executed by the simulator in detail and discuss the parameters involved. We also present a segmentation error introduction algorithm, based on polar curves and natural cubic spline interpolation, which introduces statistically realistic contouring errors. One simulation, including all I/O and preparation for rendering, takes approximately 1 minute and 40 seconds to complete on a system with 3 GB of RAM and four Intel Core 2 Quad CPUs each with a speed of 2.40 GHz. Preliminary results of our simulation suggest the maximum tolerable segmentation error given the presence of a 5.0 mm wide small tumor is between 4-5 mm. We intend to validate these results via clinical trials as part of our ongoing work.

}, keywords = {Biopsy, cancer, error, modeling, MRI, Prostate, segmentation, validation}, isbn = {0277-786X}, doi = {http://dx.doi.org/10.1117/12.844476}, author = {Shachar Avni and Siddharth Vikal and Gabor Fichtinger} }