1
|
Belue MJ, Harmon SA, Patel K, Daryanani A, Yilmaz EC, Pinto PA, Wood BJ, Citrin DE, Choyke PL, Turkbey B. Development of a 3D CNN-based AI Model for Automated Segmentation of the Prostatic Urethra. Acad Radiol 2022; 29:1404-1412. [PMID: 35183438 PMCID: PMC9339453 DOI: 10.1016/j.acra.2022.01.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/10/2022] [Accepted: 01/12/2022] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVE The combined use of prostate cancer radiotherapy and MRI planning is increasingly being used in the treatment of clinically significant prostate cancers. The radiotherapy dosage quantity is limited by toxicity in organs with de-novo genitourinary toxicity occurrence remaining unperturbed. Estimation of the urethral radiation dose via anatomical contouring may improve our understanding of genitourinary toxicity and its related symptoms. Yet, urethral delineation remains an expert-dependent and time-consuming procedure. In this study, we aim to develop a fully automated segmentation tool for the prostatic urethra. MATERIALS AND METHODS This study incorporated 939 patients' T2-weighted MRI scans (train/validation/test/excluded: 657/141/140/1 patients), including in-house and public PROSTATE-x datasets, and their corresponding ground truth urethral contours from an expert genitourinary radiologist. The AI model was developed using MONAI framework and was based on a 3D-UNet. AI model performance was determined by Dice score (volume-based) and the Centerline Distance (CLD) between the prediction and ground truth centers (slice-based). All predictions were compared to ground truth in a systematic failure analysis to elucidate the model's strengths and weaknesses. The Wilcoxon-rank sum test was used for pair-wise comparison of group differences. RESULTS The overall organ-adjusted Dice score for this model was 0.61 and overall CLD was 2.56 mm. When comparing prostates with symmetrical (n = 117) and asymmetrical (n = 23) benign prostate hyperplasia (BPH), the AI model performed better on symmetrical prostates compared to asymmetrical in both Dice score (0.64 vs. 0.51 respectively, p < 0.05) and mean CLD (2.3 mm vs. 3.8 mm respectively, p < 0.05). When calculating location-specific performance, the performance was highest at the apex and lowest at the base location of the prostate for Dice and CLD. Dice location dependence: symmetrical (Apex, Mid, Base: 0.69 vs. 0.67 vs. 0.54 respectively, p < 0.05) and asymmetrical (Apex, Mid, Base: 0.68 vs. 0.52 vs. 0.39 respectively, p < 0.05). CLD location dependence: symmetrical (Apex, Mid, Base: 1.43 mm vs. 2.15 mm vs. 3.28 mm, p < 0.05) and asymmetrical (Apex, Mid, Base: 1.83 mm vs. 3.1 mm vs. 6.24 mm, p < 0.05). CONCLUSION We developed a fully automated prostatic urethra segmentation AI tool yielding its best performance in prostate glands with symmetric BPH features. This system can potentially be used to assist treatment planning in patients who can undergo whole gland radiation therapy or ablative focal therapy.
Collapse
Affiliation(s)
- Mason J Belue
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Stephanie A Harmon
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Krishnan Patel
- Radiation Oncology Branch (K.P., D.E.C.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Enis Cagatay Yilmaz
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Peter A Pinto
- Urologic Oncology Branch (P.A.P.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Bradford J Wood
- Center for Interventional Oncology (B.J.W.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland; Department of Radiology (B.J.W.), Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Deborah E Citrin
- Radiation Oncology Branch (K.P., D.E.C.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Baris Turkbey
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland.
| |
Collapse
|
2
|
Furuncuoglu Y, Oven BB, Mert B, Yilmaz EC, Demir MK. Abdominal Variant of Lemierre's Syndrome in a Patient with Pancreatic Adenocarcinoma. Medeni Med J 2021; 36:58-62. [PMID: 33828891 PMCID: PMC8020179 DOI: 10.5222/mmj.2021.17992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 02/05/2021] [Indexed: 11/10/2022] Open
Abstract
Lemierre’s syndrome is an illness characterized by internal jugular vein thrombophlebitis related to infectious agents, primarily Fusobacterium necrophorum. These bacteria, residing in both the oropharynx and the gastrointestinal tract, may lead to pylephlebitis, a serious condition that could result in the development of hepatic abscesses. This manifestation of the disease is regarded as the abdominal variant of Lemierre’s syndrome. Patients with gastrointestinal malignancies, especially those who undergo surgeries, are susceptible to the abdominal variant of Lemierre’s syndrome. Timely diagnosis is required to avoid the life-threatening complications of the abdominal variant of Lemierre’s syndrome. Diffusion-weighted magnetic resonance imaging (MRI) might be very useful in differentiating this disease from liver metastasis in patients with malignancies. Radiologists and clinicians need to be aware of this challenging condition to prevent misdiagnosis, since prompt treatment is often lifesaving.
Collapse
Affiliation(s)
- Yavuz Furuncuoglu
- Bahçeşehir University School of Medicine, Department of Internal Medicine, Istanbul, Turkey
| | - Bala Basak Oven
- Bahçeşehir University School of Medicine, Department of Medical Oncology, Istanbul, Turkey
| | - Basak Mert
- Bahçeşehir University School of Medicine, Istanbul, Turkey
| | | | - Mustafa Kemal Demir
- Bahçeşehir University School of Medicine, Department of Radiology, Istanbul, Turkey
| |
Collapse
|
3
|
Wang H, Ceylan Koydemir H, Qiu Y, Bai B, Zhang Y, Jin Y, Tok S, Yilmaz EC, Gumustekin E, Rivenson Y, Ozcan A. Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light Sci Appl 2020; 9:118. [PMID: 32685139 PMCID: PMC7351775 DOI: 10.1038/s41377-020-00358-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 05/06/2023]
Abstract
Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.
Collapse
Affiliation(s)
- Hongda Wang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA
| | - Hatice Ceylan Koydemir
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA
| | - Yunzhe Qiu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA
| | - Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA
| | - Yibo Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA
| | - Yiyin Jin
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
| | - Sabiha Tok
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA
- Department of Biophysics, Istanbul Medical Faculty, Istanbul University, Istanbul, 22000 Turkey
| | - Enis Cagatay Yilmaz
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
| | - Esin Gumustekin
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095 USA
| | - Yair Rivenson
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA
- Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| |
Collapse
|