Dr. William J. Sehnert Profile

Dr. William J. Sehnert

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Publications (14)

Proceedings Article | 3 April 2024 Poster + Paper

Anatomical landmark detection in chest x-ray images using transformer-based networks

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12927, 129272Q (2024) https://doi.org/10.1117/12.3006881

KEYWORDS: Medical imaging, Anatomy, Biomedical applications, Chest imaging, Transformers, Image segmentation

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Proceedings Article | 2 April 2024 Presentation + Paper

Classification of endotracheal tube position in chest x-rays images

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12930, 129300L (2024) https://doi.org/10.1117/12.3006899

KEYWORDS: Medical imaging, Biomedical applications, Chest imaging, Artificial intelligence, Education and training

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Proceedings Article | 2 April 2024 Presentation + Paper

Segmentation of catheter tubes and lines in chest x-rays using deep learning models

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12930, 129300I (2024) https://doi.org/10.1117/12.3006908

KEYWORDS: Image segmentation, Medical imaging, Chest imaging, Biomedical applications, Transformers

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Proceedings Article | 28 September 2023 Presentation + Paper

Detecting landmarks in anatomical medical images using transformer-based networks

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12655, 1265508 (2023) https://doi.org/10.1117/12.2678538

KEYWORDS: Deep learning, Transformers, Medical imaging, Biomedical applications, Image segmentation, Chest imaging, Education and training

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Proceedings Article | 7 April 2023 Presentation + Paper

Feasibility of dynamic lung tomosynthesis using stationary x-ray source arrays

A. Lopez-Montes, L. Vogelsang, X. Wang, W. Sehnert, A. Sisniega, W. Zbijewski

Proceedings Volume 12463, 1246308 (2023) https://doi.org/10.1117/12.2654322

KEYWORDS: Lung, X-ray sources, Tomosynthesis, Chest, Motion blur, Medical image reconstruction

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We investigate the application of stationary Digital Tomosynthesis (DTS) using distributed x-ray source arrays for retrospectively-gated dynamic chest imaging under free-breathing conditions. The new capability might provide improved assessment of lung function impairment. A high-fidelity polychromatic x-ray system model was used to simulate a dynamic DTS configuration consisting of a linear array of 42 sources covering a 27.2° angular range in the superior-inferior direction. The array was placed 130 cm from a 42x42 cm flat-panel detector (FPD) with 0.56 mm pixels. The object center was placed at 116 cm from the source array. Dynamic acquisitions of a deformable digital chest phantom undergoing a realistic respiratory cycle (4 sec period) were simulated. The x-ray sources in the array were sequentially rastered at 8 fps; multiple passes through the array were performed to cover 3-19 breathing cycles. For reconstruction (0.5 mm voxels) of a given respiratory phase, a short sequence of projections temporally centered on the phase of interest was extracted from each cycle. The length of this sequence was varied from 1 (exact gating) to 5 frames. We investigated tradeoffs in motion blur (worse with longer gating), sampling artifacts (better with more breathing cycles), and noise, assuming a fixed total scan dose of 20 mAs regardless of the number of source array passes. A DTS acquisition over 15 respiratory cycles with exact gating yields breathing phase reconstructions at inspiration, expiration, and mid-cycle that recover >70% of the contrast in a static reconstruction and achieve adequate suppression of undersampling artifacts. For expiration and expiration, wide gating with 3 views/cycle can be used to reduce noise and improve sampling without introducing appreciable motion blur (>65% contrast recovery). For intermediate respiratory phases, wide gating causes motion blur that may require algorithmic compensation. Dynamic lung imaging with stationary DTS is feasible with approx. 1 min scan time and retrospective gating.

Proceedings Article | 15 March 2019 Presentation + Paper

Coupled active shape models for automated segmentation and landmark localization in high-resolution CT of the foot and ankle

M. Brehler, A. Islam, L. Vogelsang, D. Yang, W. Sehnert, D. Shakoor, S. Demehri, J. Siewerdsen, W. Zbijewski

Proceedings Volume 10953, 109530P (2019) https://doi.org/10.1117/12.2515022

KEYWORDS: Bone, Image segmentation, 3D modeling, Shape analysis, Data modeling, Diagnostics, 3D image processing, Biomedical engineering

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Purpose: We develop an Active Shape Model (ASM) framework for automated bone segmentation and anatomical landmark localization in weight-bearing Cone-Beam CT (CBCT). To achieve a robust shape model fit in narrow joint spaces of the foot (0.5 – 1 mm), a new approach for incorporating proximity constraints in ASM (coupled ASM, cASM) is proposed. Methods: In cASM, shape models of multiple adjacent foot bones are jointly fit to the CBCT volume. This coupling enables checking for proximity between the evolving shapes to avoid situations where a conventional single-bone ASM might erroneously fit to articular surfaces of neighbouring bones. We used 21 extremity CBCT scans of the weight-bearing foot to compare segmentation and landmark localization accuracy of ASM and cASM in leave-one-out validation. Each scan was used as a test image once; shape models of calcaneus, talus, navicular, and cuboid were built from manual surface segmentations of the remaining 20 scans. The models were augmented with seven anatomical landmarks used for common measurements of foot alignment. The landmarks were identified in the original CBCT volumes and mapped onto mean bone shape surfaces. ASM and cASM were run for 100 iterations, and the number of principal shape components was increased every 10 iterations. Automated landmark localization was achieved by applying known point correspondences between landmark vertices on the mean shape and vertices of the final active shape segmentation of the test image. Results: Root Mean Squared (RMS) error of bone surface segmentation improved from 3.6 mm with conventional ASM to 2.7 mm with cASM. Furthermore, cASM achieved convergence (no change in RMS error with iteration) after ~40 iterations of shape fitting, compared to ~60 iterations for ASM. Distance error in landmark localization was 25% to 55% lower (depending on the landmark) with cASM than with ASM. The importance of using a coupled model is underscored by the finding that cASM detected and corrected collisions between evolving shapes in 50% to 80% (depending on the bone) of shape model fits. Conclusion: The proposed cASM framework improves accuracy of shape model fits, especially in complexes of tightly interlocking, articulated joints. The approach enables automated anatomical analysis in volumetric imaging of the foot and ankle, where narrow joint spaces challenge conventional shape models.

Proceedings Article | 17 March 2011 Paper

A patient image-based technique to assess the image quality of clinical chest radiographs

Yuan Lin, Ehsan Samei, Hui Luo, James Dobbins, H. Page McAdams, Xiaohui Wang, William Sehnert, Lori Barski, David Foos

Proceedings Volume 7961, 796160 (2011) https://doi.org/10.1117/12.878765

KEYWORDS: Image quality, Lung, Image processing, Chest imaging, Chest, Imaging systems, Radiography, Databases, Image filtering, Spine

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Proceedings Article | 3 March 2010 Paper

Quantification of radiographic image quality based on patient anatomical contrast-to-noise ratio: a preliminary study with chest images

Yuan Lin, Xiaohui Wang, William Sehnert, David Foos, Lori Barski, Ehsan Samei

Proceedings Volume 7627, 76271F (2010) https://doi.org/10.1117/12.847033

KEYWORDS: Image quality, Chest, Image processing, Image enhancement, Radiography, Imaging systems, Chromium, Image analysis, Image filtering, Medical imaging

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Proceedings Article | 11 March 2008 Paper

Motion blur detection in radiographs

Hui Luo, William Sehnert, Jacquelyn Ellinwood, David Foos, Bruce Reiner, Eliot Siegel

Proceedings Volume 6914, 69140U (2008) https://doi.org/10.1117/12.770613

KEYWORDS: Motion detection, Radiography, Feature extraction, Image quality, Diagnostics, Lung, Chest, Chest imaging, Visualization, Algorithm development

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Proceedings Article | 2 March 2006 Paper

Preliminary validation of a new methodology for estimating dose reduction protocols in neonatal chest computed radiographs

Steven Don, Bruce Whiting, Charles Hildebolt, W. James Sehnert, Jacquelyn Ellinwood, Karin Töpfer, Parinaz Masoumzadeh, Richard Kraus, Keith Kronemer, Thomas Herman, William McAlister

Proceedings Volume 6142, 61421W (2006) https://doi.org/10.1117/12.654486

KEYWORDS: Liver, Image processing, Image quality, Diagnostics, Chest, Radiography, Chromium, Computer simulations, Data processing, Radiology

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Proceedings Article | 12 April 2005 Paper

Effect of grayscale resolution on the performance of lung nodule detection on a softcopy display

W. James Sehnert, Brian Keelan, Karin Toepfer

Proceedings Volume 5744, (2005) https://doi.org/10.1117/12.593766

KEYWORDS: Heart, Lung, LCDs, Calibration, Video, Sensors, Image resolution, Chest, X-rays, Selenium

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Proceedings Article | 6 April 2005 Paper

Observer study of a noise suppression algorithm for computed radiography images

Mary Couwenhoven, William Sehnert, Xiaohui Wang, Michael Dupin, John Wandtke, Steven Don, Richard Kraus, Narinder Paul, Neil Halin, Robert Sarno

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.595159

KEYWORDS: Control systems, Diagnostics, Image processing, Signal to noise ratio, Radiography, X-rays, Image enhancement, Image quality, Chest, Chromium

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Proceedings Article | 4 May 2004 Paper

Relative impact of detector noise and anatomical structure on lung nodule detection

Brian Keelan, Karin Topfer, John Yorkston, William Sehnert, Jacquelyn Ellinwood

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.536733

KEYWORDS: Lung, Sensors, Heart, Signal to noise ratio, X-rays, X-ray imaging, Modulation transfer functions, Chest, X-ray detectors, Signal detection

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Proceedings Article | 8 May 1995 Paper

From the finite-length line-spread function to the point-spread function

William Sehnert

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208370

KEYWORDS: Point spread functions, Fourier transforms, Convolution, Imaging systems, Image segmentation, Statistical analysis, Error analysis, Information operations, Health sciences, Radiology

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Showing 5 of 14 publications

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