Dr. Sharmin Sultana Profile

Dr. Sharmin Sultana

Postdoctoral Researcher at Johns Hopkins Univ School of Medicine

SPIE Involvement:

Author

Publications (5)

SPIE Journal Paper | 14 October 2020 Open Access

Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network

Sharmin Sultana, Adam Robinson, Daniel Y. Song, Junghoon Lee

JMI, Vol. 7, Issue 05, 055001, (October 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.5.055001

KEYWORDS: Image segmentation, Computed tomography, Prostate, Rectum, Gallium nitride, Bladder, 3D modeling, Convolutional neural networks, Cancer, 3D image processing

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Purpose: Accurate segmentation of treatment planning computed tomography (CT) images is important for radiation therapy (RT) planning. However, low soft tissue contrast in CT makes the segmentation task challenging. We propose a two-step hierarchical convolutional neural network (CNN) segmentation strategy to automatically segment multiple organs from CT. Approach: The first step generates a coarse segmentation from which organ-specific regions of interest (ROIs) are produced. The second step produces detailed segmentation of each organ. The ROIs are generated using UNet, which automatically identifies the area of each organ and improves computational efficiency by eliminating irrelevant background information. For the fine segmentation step, we combined UNet with a generative adversarial network. The generator is designed as a UNet that is trained to segment organ structures and the discriminator is a fully convolutional network, which distinguishes whether the segmentation is real or generator-predicted, thus improving the segmentation accuracy. We validated the proposed method on male pelvic and head and neck (H&N) CTs used for RT planning of prostate and H&N cancer, respectively. For the pelvic structure segmentation, the network was trained to segment the prostate, bladder, and rectum. For H&N, the network was trained to segment the parotid glands (PG) and submandibular glands (SMG). Results: The trained segmentation networks were tested on 15 pelvic and 20 H&N independent datasets. The H&N segmentation network was also tested on a public domain dataset (N = 38) and showed similar performance. The average dice similarity coefficients (mean ± SD) of pelvic structures are 0.91 ± 0.05 (prostate), 0.95 ± 0.06 (bladder), 0.90 ± 0.09 (rectum), and H&N structures are 0.87 ± 0.04 (PG) and 0.86 ± 0.05 (SMG). The segmentation for each CT takes <10 s on average. Conclusions: Experimental results demonstrate that the proposed method can produce fast, accurate, and reproducible segmentation of multiple organs of different sizes and shapes and show its potential to be applicable to different disease sites.

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Proceedings Article | 16 March 2020 Presentation + Paper

CNN-based hierarchical coarse-to-fine segmentation of pelvic CT images for prostate cancer radiotherapy

Sharmin Sultana, Adam Robinson, Daniel Song, Junghoon Lee

Proceedings Volume 11315, 113151I (2020) https://doi.org/10.1117/12.2549979

KEYWORDS: Image segmentation, Prostate, Computed tomography, Bladder, Rectum, Radiotherapy, Prostate cancer, Convolutional neural networks

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SPIE Journal Paper | 12 September 2019

Deformable registration of PET/CT and ultrasound for disease-targeted focal prostate brachytherapy

Sharmin Sultana, Daniel Song, Junghoon Lee

JMI, Vol. 6, Issue 03, 035003, (September 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.3.035003

KEYWORDS: Prostate, Image registration, Computed tomography, Image segmentation, Ultrasonography, Prostate cancer, Image fusion, Positron emission tomography, Rigid registration, Magnetic resonance imaging

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