As one of the critical branches of medical image processing, the task of segmentation of breast cancer tumors is of great importance for planning surgical interventions, radiotherapy and chemotherapy. Breast cancer tumor segmentation faces several challenges, including the inherent complexity and heterogeneity of breast tissue, the presence of various imaging artifacts and noise in medical images, low contrast between the tumor region and healthy tissue, and inconsistent size of the tumor region. Furthermore, the existing segmentation methods may not fully capture the rich spatial and contextual information in small-sized regions in breast images, leading to suboptimal performance. In this paper, we propose a novel breast tumor segmentation method, called the transformer and graph convolutional neural (TS-GCN) network, for medical imaging analysis. Specifically, we designed a feature aggregation network to fuse the features extracted from the transformer, GCN and convolutional neural network (CNN) networks. The CNN extract network is designed for the image's local deep feature, and the transformer and GCN networks can better capture the spatial and context dependencies among pixels in images. By leveraging the strengths of three feature extraction networks, our method achieved superior segmentation performance on the BUSI dataset and dataset B. The TS-GCN showed the best performance on several indexes, with Acc of 0.9373, Dice of 0.9058, IoU of 0.7634, F1 score of 0.9338, and AUC of 0.9692, which outperforms other state-of-the-art methods. The research of this segmentation method provides a promising future for medical image analysis and diagnosis of other diseases.
Citation: Haiyan Song, Cuihong Liu, Shengnan Li, Peixiao Zhang. TS-GCN: A novel tumor segmentation method integrating transformer and GCN[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18173-18190. doi: 10.3934/mbe.2023807
| [1] | Masahiro Seike . Histamine suppresses T helper 17 responses mediated by transforming growth factor-β1 in murine chronic allergic contact dermatitis. AIMS Allergy and Immunology, 2018, 2(4): 180-189. doi: 10.3934/Allergy.2018.4.180 |
| [2] | Kamyar Nasiri, Zohreh Mortezania, Sanaz Oftadehbalani, Mohammad Khosousi Sani, Amin Daemi, Seyyedeh Touran Hosseini, Yusuf Döğüş, Zafer Yönden . Parasitology and Covid-19: epidemiological, immunological, pathological, and therapeutic aspects. AIMS Allergy and Immunology, 2023, 7(1): 92-103. doi: 10.3934/Allergy.2023007 |
| [3] | Gozde Arslan, Ayla Balci, Rahmi Cubuk, Feyzullah Cetinkaya . Knowledge and experience of radiologists working in Istanbul on radiographic contrast medium anaphylaxis. AIMS Allergy and Immunology, 2017, 1(2): 71-77. doi: 10.3934/Allergy.2017.2.71 |
| [4] | Lucie Mondoulet, Sophie Wavrin, Vincent Dioszeghy, Véronique Dhelft, Emilie Puteaux, Mélanie Ligouis, Camille Plaquet, Christophe Dupont, Pierre-Henri Benhamou . No impact of filaggrin deficiency on the efficacy of epicutaneous immunotherapy in a murine model. AIMS Allergy and Immunology, 2017, 1(1): 1-14. doi: 10.3934/Allergy.2017.1.1 |
| [5] | Ryuta Muromoto, Kenji Oritani, Tadashi Matsuda . IL-17 signaling is regulated through intrinsic stability control of mRNA during inflammation. AIMS Allergy and Immunology, 2022, 6(3): 188-199. doi: 10.3934/Allergy.2022014 |
| [6] | Fuyong Jiao, Yan Pan, JAY N SHAH, Jiale Wang, Kaisheng Xie, Zhilong Mu, Qing Wei . Challenges and management of bleeding in Kawasaki disease with aspirin treatment. AIMS Allergy and Immunology, 2024, 8(4): 324-332. doi: 10.3934/Allergy.2024020 |
| [7] | Chunyan Li, Wojciech Dawicki, Xiaobei Zhang, Chris Rudulier, John R. Gordon . IL-10- and retinoic acid-induced regulatory dendritic cells are therapeutically equivalent in mouse models of asthma and food allergy. AIMS Allergy and Immunology, 2021, 5(2): 73-91. doi: 10.3934/Allergy.2021007 |
| [8] | Gianna Moscato, Gianni Pala . Occupational allergy to food-derived allergens. AIMS Allergy and Immunology, 2017, 1(1): 21-30. doi: 10.3934/Allergy.2017.1.21 |
| [9] | Tamara Tuuminen, Jouni Lohi . Immunological and toxicological effects of bad indoor air to cause Dampness and Mold Hypersensitivity Syndrome. AIMS Allergy and Immunology, 2018, 2(4): 190-204. doi: 10.3934/Allergy.2018.4.190 |
| [10] | Swathi Mukurala, Jahanavi Bandla, Swetha kappala . Angioedema-post COVID symptoms. AIMS Allergy and Immunology, 2023, 7(4): 273-280. doi: 10.3934/Allergy.2023018 |
As one of the critical branches of medical image processing, the task of segmentation of breast cancer tumors is of great importance for planning surgical interventions, radiotherapy and chemotherapy. Breast cancer tumor segmentation faces several challenges, including the inherent complexity and heterogeneity of breast tissue, the presence of various imaging artifacts and noise in medical images, low contrast between the tumor region and healthy tissue, and inconsistent size of the tumor region. Furthermore, the existing segmentation methods may not fully capture the rich spatial and contextual information in small-sized regions in breast images, leading to suboptimal performance. In this paper, we propose a novel breast tumor segmentation method, called the transformer and graph convolutional neural (TS-GCN) network, for medical imaging analysis. Specifically, we designed a feature aggregation network to fuse the features extracted from the transformer, GCN and convolutional neural network (CNN) networks. The CNN extract network is designed for the image's local deep feature, and the transformer and GCN networks can better capture the spatial and context dependencies among pixels in images. By leveraging the strengths of three feature extraction networks, our method achieved superior segmentation performance on the BUSI dataset and dataset B. The TS-GCN showed the best performance on several indexes, with Acc of 0.9373, Dice of 0.9058, IoU of 0.7634, F1 score of 0.9338, and AUC of 0.9692, which outperforms other state-of-the-art methods. The research of this segmentation method provides a promising future for medical image analysis and diagnosis of other diseases.
Non-tuberculous mycobacteria (NTM)-induced nodules following botulinum toxin injections are increasingly being reported [1]–[3]. These infections often present as persistent nodules, which can be challenging to diagnose and manage [3]–[5]. This report presents a rare case of Mycobacterium chelonae-induced granulomatous nodules following botulinum toxin injections and includes a comprehensive review of the literature on NTM infections specifically related to botulinum toxin procedures. The aim is to highlight the importance of accurate diagnosis, appropriate antimicrobial therapy, and to provide an overview of reported cases to inform clinical practice.
A 35-year-old healthy male presented with multiple tender and painful nodules localized at injection sites on the forehead and periorbital areas. These nodules appeared three weeks after receiving Onabotulinum toxin injections from an untrusted provider. Initially, the patient was treated with oral steroids by his initial provider without any improvement. On examination, the nodules were tender and erythematous, confined to the injection sites, and no lymphadenopathy was observed (Figure 1a,b). Empiric antibiotic therapy with oral ciprofloxacin 500 mg twice daily and clindamycin 900 mg daily was initiated to cover potential bacterial infections. However, there was no improvement after three days of treatment. A biopsy was performed on the largest nodule. The specimen was sent for histopathology to rule out mycobacterial infection or sarcoidosis. Additional samples were sent for mycobacterial, bacterial, and fungal cultures, along with NTM PCR testing. Histopathological analysis revealed interstitial granulomatous inflammation with mild necrosis, indicative of a suppurative granuloma (Figure 1c). NTM PCR confirmed the presence of Mycobacterium chelonae. Antimicrobial susceptibility testing was performed, showing no resistance. Based on the diagnosis of NTM-induced granulomatous nodules, the patient was started on a combination therapy of oral clarithromycin 500 mg, levofloxacin 500 mg, and trimethoprim/sulfamethoxazole 160 mg/800 mg, all administered twice daily for six months. Significant improvement was noted six weeks into the therapy, with total remission observed at four months. At an eight-month follow-up, the patient showed no signs of relapse.
A comprehensive review of the literature identified eight reported cases of botulinum toxin-induced granulomatous nodules attributed to NTM infection. The key characteristics of these cases are summarized in Table 1.
The eight cases involved female patients with a mean age of 38 years. Six cases were associated with botulinum toxin injections for wrinkle treatment, while two cases were related to injections for masseter hypertrophy. The nodules typically appeared an average of 20 days post-injection and were characterized by painful, erythematous, and violaceous lesions, with some cases progressing to abscess formation [1]–[7].
Of the eight cases, six underwent biopsy, which revealed suppurative granulomas [1]–[4],[6],[7]. In the remaining two cases, a biopsy was not performed due to positive culture results [1],[2]. All isolated organisms were identified as NTM. Specifically, five cases were caused by Mycobacterium abscessus [2]–[5], one case by Mycobacterium immunogenum [7], and one case was culture and PCR-negative but treated empirically based on strong clinical and histological suspicion [6]. One case presented with acid-fast positive bacilli on histology, but the specific NTM species was not identified [1]. Notably, nodules caused by M. abscessus appeared between 4 to 10 days post-injection, while the nodule due to M. immunogenum emerged 3.5 months after the procedure [2]–[5],[7].
To date, no previous cases of M. chelonae associated with botulinum toxin-induced granulomatous nodules have been reported in the literature. The identification of this organism in our case thus represents the first documented instance of M. chelonae as an etiological agent in such a context.
| Study | Age & Sex | Toxin Source | Clinical Presentation | Histology | Organism | Treatment | Outcome |
| Li et al. | 39/F | NA | Painful nodules at injection sites 1 week later | Necrotic granuloma | M. abscessus (PCR) | Levofloxacin 500 mg BID, clarithromycin 500 mg BID (stopped in 1 week), surgical excision | NA |
| Thanasarnaksorn et al. | 42/F | Neuronox® | Tender nodules at all sites of injection 4 days later | Suppurative granuloma | Negative culture and PCR for NTM | Clarithromycin 500 mg/day, levofloxacin 500 mg/day for 6 months (empirically) | Major improvement at 6 weeks, full resolution at 6 months |
| Deng et al. | 53/F | NA | Painful nodules and abscesses at injection sites 10 days later | Suppurative granuloma | M. abscessus (culture) | Amikacin 200 mg, clarithromycin 500 mg, moxifloxacin 400 mg for 7 months | Full resolution after 7 months |
| Chen et al. | 32/F | Non-trusted source | Painful papules and nodules on all injection sites 1 week later | Suppurative granuloma | M. abscessus (culture) | Prednisone 30 mg/d, intravenous azithromycin, clarithromycin 250 mg BID, rifampicin 450 mg daily, ethambutol 250 mg TID for 3 months | Full resolution after 3 months with atrophic scars and PIH |
| Chen et al. | 34/F | Non-trusted source | Painful papules and nodules on lower jaw, malar, and temple region 10 days later | Biopsy not done | M. abscessus (culture) | Intralesional corticosteroids, oral penicillin, gentamicin, debridement, clarithromycin 250 mg BID, rifampicin 450 mg daily for nearly 6 months | Full resolution after 6 months with scarring and PIH |
| Yeon et al. | 34/F | Unknown source | Tender nodule over the left mandible after 3.5 months | Suppurative granuloma | M. immunogenum (culture) | Phenoxymethylpenicillin, clindamycin, clarithromycin 500 mg BID for 6 months | Full resolution |
| Saeb-Lima et al. | 45/F | NA | Tender nodules at points of injection 1 week later | Suppurative granuloma with acid-fast positive bacilli | Culture not done | Rifampicin, clarithromycin, azithromycin for 40 days | Full resolution with PIH |
| Ou et al. | 25/F | Unknown source | Painful nodules on cheeks 10 days later | Biopsy not done | M. abscessus (culture) | Azithromycin for 2 weeks, surgical excision, clarithromycin 300 mg for 6 months | Full resolution with dog-ear deformity after excision |
DownLoad:
CSV
In patients presenting with nodules after botulinum toxin injections, initial management can include oral antibiotics and topical treatments [8],[9]. It is crucial to avoid starting oral steroids empirically due to the high risk of infectious nodules compared to non-infectious ones. Biopsy remains the gold standard for determining the etiology of the nodule. If the nodule is found to be inflammatory, such as in sarcoidal nodules, systemic steroids along with immunosuppressive agents may be administered [8],[10]–[14]. However, if the histology shows suppurative or necrotic features, NTM infection should be ruled out using special stains, cultures, and PCR [9].
For infectious granulomas with positive NTM PCR or culture results, treatment should be tailored according to antibiotic sensitivity profiles. Since M. abscessus is the most frequently isolated organism and is known for its rapid growth and high resistance rates, combination therapy involving at least two antimicrobial agents is recommended for a duration of 4 to 6 months. Preferred treatments include a macrolide, such as clarithromycin, paired with agents like amikacin, levofloxacin, cefoxitin or tigecycline [15]–[17].
In the single case involving M. immunogenum, successful treatment was achieved with clarithromycin 500 mg twice daily for six months [7]. Antimicrobial therapy alone led to complete remission in five cases, though post-inflammatory hyperpigmentation (PIH) was noted in three instances [1],[2]. When antimicrobial therapy proved insufficient, surgical excision or debridement was performed in three cases, despite the higher risk of scarring associated with these procedures [2],[4],[5].
Given that negative cultures and PCR results are not uncommon in NTM infections, it is crucial to consider NTM infection as a potential cause of suppurative granulomas with negative test results. For cases with strong clinical and histological suspicion, multiple specimens should be tested through culture and PCR if initial tests are negative. Alternatively, empirical treatment can be initiated based on clinical and histological findings [15],[16]. This approach was exemplified by Thanasarnaksorn et al., who achieved full remission with a six-month regimen of clarithromycin and levofloxacin despite initial negative test results [6].
This case, along with the review of the literature on NTM infections following botulinum toxin injections, underscores the necessity for clinicians to consider NTM infections in patients presenting with persistent nodules post-procedure, particularly when initial treatments fail. This is the first reported case of M. chelonae as the etiological agent in such a context. To prevent such infections, it is crucial for patients to seek treatment from trusted and licensed providers who use authentic products and perform procedures in sterile environments.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
| [1] |
M. H. Yap, G. Pons, J. Marti, S. Ganau, M. Sentis, R. Zwiggelaar, et al., Automated breast ultrasound lesions detection using convolutional neural networks, IEEE J. Biomed. Health Inf., 22 (2018), 1218–1226. https://doi.org/10.1109/JBHI.2017.2731873 doi: 10.1109/JBHI.2017.2731873
|
| [2] |
J. Gao, Q. Jiang, B. Zhou, D. Chen, Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview, Math. Biosci. Eng., 16 (2019), 6536–6561. https://doi.org/10.3934/mbe.2019326 doi: 10.3934/mbe.2019326
|
| [3] |
C. Xu, Y. Qi, Y. Wang, M. Lou, J. Pi, Y. Ma, ARF-Net: An adaptive receptive gield network for breast mass segmentation in whole mammograms and ultrasound images, Biomed. Signal Process. Control, 71 (2022), 103178. https://doi.org/10.1016/j.bspc.2021.103178 doi: 10.1016/j.bspc.2021.103178
|
| [4] |
Y. Wang, N. Wang, M. Xu, J. Yu, C. Qin, X. Luo, et al., Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound, IEEE Trans. Med. Imaging, 39 (2019), 866–876. https://doi.org/10.1109/TMI.2019.2936500 doi: 10.1109/TMI.2019.2936500
|
| [5] |
S. Jiang, J. Li, Z. Hua, Transformer with progressive sampling for medical cellular image segmentation, Math. Biosci. Eng., 19 (2022), 12104–12126. https://doi.org/10.3934/mbe.2022563 doi: 10.3934/mbe.2022563
|
| [6] |
A. Iqbal, M. Sharif, MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images, J. King Saud Univ. Comput. Inf. Sci., 34 (2022), 7283–7299. http://dx.doi.org/10.1016/j.jksuci.2021.10.002 doi: 10.1016/j.jksuci.2021.10.002
|
| [7] |
R. Bi, C. Ji, Z. Yang, M. Qiao, P. Lv, H. Wang, Residual-based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT, Math. Biosci. Eng., 19 (2022), 4703–4718. https://doi.org/10.3934/mbe.2022219 doi: 10.3934/mbe.2022219
|
| [8] |
B. Lei, S. Huang, R. Li, C. Bian, H. Li, Y. H. Chou, et al., Segmentation of breast anatomy for automated whole breast ultrasound images with boundary regularized convolutional encoder-decoder network, Neurocomputing, 321 (2018), 178–186. https://doi.org/10.1016/j.neucom.2018.09.043 doi: 10.1016/j.neucom.2018.09.043
|
| [9] |
Y. Ouyang, Z. Zhou, W. Wu, J. Tian, F. Xu, S. Wu, et al., A review of ultrasound detection methods for breast microcalcification, Math. Biosci. Eng., 16 (2019), 1761–1785. https://doi.org/10.3934/mbe.2019085 doi: 10.3934/mbe.2019085
|
| [10] |
M. N. S. K. B. Soulami, N. Kaabouch, A. Tamtaoui, Breast cancer: One-stage automated detection, segmentation, and classification of digital mammograms using U-net model based semantic segmentation, Biomed. Signal Process. Control, 2021 (2021), 102481. https://doi.org/10.1016/j.bspc.2021.102481 doi: 10.1016/j.bspc.2021.102481
|
| [11] |
Y. Wang, N. Wang, M. Xu, J. Yu, C. Qin, X. Luo, et al., Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound, IEEE Trans. Med. Imaging, 39 (2019), 866–876. https://doi.org/10.1109/TMI.2019.2936500 doi: 10.1109/TMI.2019.2936500
|
| [12] |
E. H. Houssein, M. M. Emam, A. A. Ali, P. N. Suganthan, Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review, Exp. Syst. Appl., 167 (2021), 114161. https://doi.org/10.1016/j.eswa.2020.114161 doi: 10.1016/j.eswa.2020.114161
|
| [13] |
M. Xian, Y. Zhang, H. D. Cheng, F. Xu, B. Zhang, J. Ding, Automatic breast ultrasound image segmentation: A survey, Pattern Recognit., 79 (2018), 340–355. https://doi.org/10.1016/j.patcog.2018.02.012 doi: 10.1016/j.patcog.2018.02.012
|
| [14] |
Y. Tong, Y. Liu, M. Zhao, L. Meng, J. Zhang, Improved U-net MALF model for lesion segmentation in breast ultrasound images, Biomed. Signal Process. Control, 68 (2021), 102721. https://doi.org/10.1016/j.bspc.2021.102721 doi: 10.1016/j.bspc.2021.102721
|
| [15] |
D. Mishra, S. Chaudhury, M. Sarkar, A. S. Soin, Ultrasound image segmentation: A deeply supervised network with attention to boundaries, IEEE Trans. Biomed. Eng., 66 (2018), 1637–1648. https://doi.org/10.1109/TBME.2018.2877577 doi: 10.1109/TBME.2018.2877577
|
| [16] |
G. Chen, Y. Dai, J. Zhang, C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation, Comput. Methods Programs Biomed., 2022 (2022), 107086. https://doi.org/10.1016/j.cmpb.2022.107086 doi: 10.1016/j.cmpb.2022.107086
|
| [17] |
N. K. Tomar, D. Jha, M. A. Riegler, H. D. Johansen, D. Johansen, J. Rittscher, et al., Fanet: A feedback attention network for improved biomedical image segmentation, Trans. Neural Networks Learn. Syst., 2022 (2022). https://doi.org/10.1109/TNNLS.2022.3159394 doi: 10.1109/TNNLS.2022.3159394
|
| [18] |
L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, Trans. Pattern Anal. Mach. Intell., 2018 (2018), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184 doi: 10.1109/TPAMI.2017.2699184
|
| [19] | Y. Xie, J. Zhang, C. Shen, Y. Xia, Cotr: Efficiently bridging CNN and Transformer for 3d medical image segmentation, in Medical Image Computing and Computer Assisted Intervention MICCAI, (2021), 171–180. https://doi.org/10.1007/978-3-030-87199-4_16 |
| [20] |
N. S. Punn, S. Agarwal, RCA-IUnet: A residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging, Mach. Vision Appl., 33 (2022), 1–10. ttps://doi.org/10.1007/s00138-022-01280-3 doi: 10.1007/s00138-021-01257-8
|
| [21] |
N. Abraham, N. M. B. T. Khan, A novel focal tversky loss function with improved attention U-Net for lesion segmentation, Int. Symp. Biomed. Imaging, 2019 (2019), 683–687. https://doi.org/10.1109/ISBI.2019.8759329 doi: 10.1109/ISBI.2019.8759329
|
| [22] | W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, J. Tang, Node similarity preserving graph convolutional networks, in Proceedings of the 14th ACM International Conference on Web Search and Data Mining, (2021), 148–156. https://doi.org/10.1145/3437963.3441735 |
| [23] | B. Wu, X. Liang, X. Zheng, Y. Guo, H. Tang, Improving dynamic graph convolutional network with fine-grained attention mechanism, in ICASSP International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2022), 3938–3942. https://doi.org/10.1109/ICASSP43922.2022.9746009 |
| [24] | Y. Lu, Y. Chen, D. Zhao, J. Chen, Graph-FCN for image semantic segmentation, in Advances in Neural Networks–ISNN 2019: 16th International Symposium on Neural Networks, (2019), 97–105. https://doi.org/10.1007/978-3-030-22796-8_11 |
| [25] | Y. Huang, Y. Sugano, Y. Sato, Improving action segmentation via graph-based temporal reasoning, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2020), 14024–14034. https://doi.org/10.1109/CVPR42600.2020.01404 |
| [26] |
W. Al-Dhabyani, M. Gomaa, H. Khaled, A. Fahmy, Dataset of breast ultrasound images, Data Brief, 28 (2020), 104863. https://doi.org/10.1016/j.dib.2019.104863 doi: 10.1016/j.dib.2019.104863
|
| [27] |
Z. Fu, J. Zhang, R. Luo, Y. Sun, D. Deng, L. Xia, TF-Unet: An automatic cardiac MRI image segmentation method, Math. Biosci. Eng., 19 (2022), 5207–5222. https://doi.org/10.3934/mbe.2022244 doi: 10.3934/mbe.2022244
|
| [28] |
X. Xu, M. Zhao, P. Shi, R. Ren, X. He, X. Wei, et al., Crack detection and comparison study based on faster R-CNN and mask R-CNN, Sensors, 22 (2022), 1215. https://doi.org/10.3390/s22031215 doi: 10.3390/s22031215
|
| [29] | L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in Proceedings of the European Conference on Computer Vision (ECCV), (2018), 801–818. |
| [30] |
R. Huang, M. Lin, H. Dou, Z. Lin, Q. Ying, X. Jia, et al., Boundary-rendering network for breast lesion segmentation in ultrasound images, Med. Image Anal., 80 (2022), 102478. https://doi.org/10.1016/j.media.2022.102478 doi: 10.1016/j.media.2022.102478
|
| [31] | Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, J. Liang, UNet++: A nested U-net architecture for medical image segmentation, in Lecture Notes in Computer Science, (2018), 3–11. http: /dx.doi.org/10.1007/ 978-3-030-00889-51 |
| [32] | E. Sanderson, B. J. Matuszewski, FCN-Transformer feature fusion for polyp segmentation, in Medical Image Understanding and Analysis: 26th Annual Conference, Springer International Publishing, (2022), 892–907. https://doi.org/10.1007/978-3-031-12053-4_65 |
| [33] |
X. Feng, T. Wang, X. Yang, M. Zhang, W. Guo, W. Wang, ConvWin-UNet: UNet-like hierarchical vision transformer combined with convolution for medical image segmentation, Math. Biosci. Eng., 20 (2023), 128–144. https://doi.org/10.3934/mbe.2023007 doi: 10.3934/mbe.2023007
|
| [34] | O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, et al., Attention U-Net: learning where to look for the pancreas, preprint, arXiv: 1804.03999. https://doi.org/10.48550/arXiv.1804.03999 |
| [35] |
X. Zhang, K. Liu, K. Zhang, X. Li, Z. Sun, B. Wei, SAMS-Net: Fusion of attention mechanism and multi-scale features network for tumor infiltrating lymphocytes segmentation, Math. Biosci. Eng., 20 (2023), 2964–2979. https://doi.org/10.3934/mbe.2023140 doi: 10.3934/mbe.2023140
|
| [36] |
R. K. Meleppat, P. Zhang, M. J. Ju, S. K. K. Manna, Y. Jian, E. N. Pugh, et al., Directional optical coherence tomography reveals melanin concentration-dependent scattering properties of retinal pigment epithelium, J. Biomed. Optics, 24 (2019), 066011. https://doi.org/10.1117/1.JBO.24.6.066011 doi: 10.1117/1.JBO.24.6.066011
|
| [37] |
R. K. Meleppat, C. R. Fortenbach, Y. Jian, E. S. Martinez, K. Wagner, B. S. Modjtahedi, et al., In Vivo imaging of retinal and choroidal morphology and vascular plexuses of vertebrates using swept-source optical coherence tomography, Trans. Vis. Sci. Tech., 11 (2022), 11. https://doi.org/10.1167/tvst.11.8.11 doi: 10.1167/tvst.11.8.11
|
| [38] |
R. K. Meleppat, K. E. Ronning, S. J. Karlen, K. K. Kothandath, M. E. Burns, E. N. Pugh, et al., In situ morphologic and spectral characterization of retinal pigment epithelium organelles in mice using multicolor confocal fluorescence imaging, Invest. Ophthalmol. Vis. Sci., 61 (2020), 1. https://doi.org/10.1167/iovs.61.13.1 doi: 10.1167/iovs.61.13.1
|
| [39] |
J. He, Q. Zhu, K. Zhang, P. Yu, J. Tang, An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation, Appl. Soft Comput., 113 (2021), 107947. https://doi.org/10.1016/j.asoc.2021.107947 doi: 10.1016/j.asoc.2021.107947
|
| [40] |
X. Liu, D. Zhang, J. Yao, J. Tang, Transformer and convolutional based dual branch network for retinal vessel segmentation in OCTA images, Biomed. Signal Process. Control, 83 (2023), 104604. https://doi.org/10.1016/j.bspc.2023.104604 doi: 10.1016/j.bspc.2023.104604
|
| [41] |
C. Zhao, A. Vij, S. Malhotra, J. Tang, H. Tang, D. Pienta, et al., Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms, Comput. Biol. Med., 136 (2021), 104667. https://doi.org/10.1016/j.compbiomed.2021.104667 doi: 10.1016/j.compbiomed.2021.104667
|
| 1. | Robert J. Hill, Radoslaw Trojanek, An evaluation of competing methods for constructing house price indexes: The case of Warsaw, 2022, 120, 02648377, 106226, 10.1016/j.landusepol.2022.106226 | |
| 2. | Junhao Zhong, Zhenzhen Wang, Artificial intelligence techniques for financial distress prediction, 2022, 7, 2473-6988, 20891, 10.3934/math.20221145 | |
| 3. | 2023, 1813-9450, 10.1596/1813-9450-10301 | |
| 4. | Min Shu, Ruiqiang Song, Detection of financial bubbles using a log-periodic power law singularity (LPPLS) model, 2024, 1556-5068, 10.2139/ssrn.4734944 | |
| 5. | Radoslaw Trojanek, Michal Gluszak, Justyna Tanas, Alex van de Minne, Detecting housing bubble in Poland: Investigation into two housing booms, 2023, 140, 01973975, 102928, 10.1016/j.habitatint.2023.102928 | |
| 6. | Sandro Heiniger, Winfried Koeniger, Michael Lechner, The heterogeneous response of real estate prices during the Covid-19 pandemic, 2024, 0964-1998, 10.1093/jrsssa/qnae078 | |
| 7. | Michał Hebdzyński, Are Hedonic Models Really Quality-Adjusted? The Role of Apartment Quality in Hedonic Models of Housing Rental Market, 2024, 32, 2300-5289, 46, 10.2478/remav-2024-0014 | |
| 8. | Min Shu, Ruiqiang Song, Detection of financial bubbles using a log‐periodic power law singularity (LPPLS) model, 2024, 16, 1939-5108, 10.1002/wics.1649 | |
| 9. | Michał Hebdzyński, Quality information gaps in housing listings: Do words mean the same as pictures?, 2023, 38, 1566-4910, 2399, 10.1007/s10901-023-10043-z | |
| 10. | Roland Füss, Kathleen Kürschner Rauck, Alois Weigand, Housing Price Externalities of Photovoltaic Systems: The Relevance of View, 2023, 1556-5068, 10.2139/ssrn.4618784 |
Figures(4) / Tables(3)
Haiyan Song, Cuihong Liu, Shengnan Li, Peixiao Zhang. TS-GCN: A novel tumor segmentation method integrating transformer and GCN[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18173-18190. doi: 10.3934/mbe.2023807
| Study | Age & Sex | Toxin Source | Clinical Presentation | Histology | Organism | Treatment | Outcome |
| Li et al. | 39/F | NA | Painful nodules at injection sites 1 week later | Necrotic granuloma | M. abscessus (PCR) | Levofloxacin 500 mg BID, clarithromycin 500 mg BID (stopped in 1 week), surgical excision | NA |
| Thanasarnaksorn et al. | 42/F | Neuronox® | Tender nodules at all sites of injection 4 days later | Suppurative granuloma | Negative culture and PCR for NTM | Clarithromycin 500 mg/day, levofloxacin 500 mg/day for 6 months (empirically) | Major improvement at 6 weeks, full resolution at 6 months |
| Deng et al. | 53/F | NA | Painful nodules and abscesses at injection sites 10 days later | Suppurative granuloma | M. abscessus (culture) | Amikacin 200 mg, clarithromycin 500 mg, moxifloxacin 400 mg for 7 months | Full resolution after 7 months |
| Chen et al. | 32/F | Non-trusted source | Painful papules and nodules on all injection sites 1 week later | Suppurative granuloma | M. abscessus (culture) | Prednisone 30 mg/d, intravenous azithromycin, clarithromycin 250 mg BID, rifampicin 450 mg daily, ethambutol 250 mg TID for 3 months | Full resolution after 3 months with atrophic scars and PIH |
| Chen et al. | 34/F | Non-trusted source | Painful papules and nodules on lower jaw, malar, and temple region 10 days later | Biopsy not done | M. abscessus (culture) | Intralesional corticosteroids, oral penicillin, gentamicin, debridement, clarithromycin 250 mg BID, rifampicin 450 mg daily for nearly 6 months | Full resolution after 6 months with scarring and PIH |
| Yeon et al. | 34/F | Unknown source | Tender nodule over the left mandible after 3.5 months | Suppurative granuloma | M. immunogenum (culture) | Phenoxymethylpenicillin, clindamycin, clarithromycin 500 mg BID for 6 months | Full resolution |
| Saeb-Lima et al. | 45/F | NA | Tender nodules at points of injection 1 week later | Suppurative granuloma with acid-fast positive bacilli | Culture not done | Rifampicin, clarithromycin, azithromycin for 40 days | Full resolution with PIH |
| Ou et al. | 25/F | Unknown source | Painful nodules on cheeks 10 days later | Biopsy not done | M. abscessus (culture) | Azithromycin for 2 weeks, surgical excision, clarithromycin 300 mg for 6 months | Full resolution with dog-ear deformity after excision |
DownLoad:
CSV
| Study | Age & Sex | Toxin Source | Clinical Presentation | Histology | Organism | Treatment | Outcome |
| Li et al. | 39/F | NA | Painful nodules at injection sites 1 week later | Necrotic granuloma | M. abscessus (PCR) | Levofloxacin 500 mg BID, clarithromycin 500 mg BID (stopped in 1 week), surgical excision | NA |
| Thanasarnaksorn et al. | 42/F | Neuronox® | Tender nodules at all sites of injection 4 days later | Suppurative granuloma | Negative culture and PCR for NTM | Clarithromycin 500 mg/day, levofloxacin 500 mg/day for 6 months (empirically) | Major improvement at 6 weeks, full resolution at 6 months |
| Deng et al. | 53/F | NA | Painful nodules and abscesses at injection sites 10 days later | Suppurative granuloma | M. abscessus (culture) | Amikacin 200 mg, clarithromycin 500 mg, moxifloxacin 400 mg for 7 months | Full resolution after 7 months |
| Chen et al. | 32/F | Non-trusted source | Painful papules and nodules on all injection sites 1 week later | Suppurative granuloma | M. abscessus (culture) | Prednisone 30 mg/d, intravenous azithromycin, clarithromycin 250 mg BID, rifampicin 450 mg daily, ethambutol 250 mg TID for 3 months | Full resolution after 3 months with atrophic scars and PIH |
| Chen et al. | 34/F | Non-trusted source | Painful papules and nodules on lower jaw, malar, and temple region 10 days later | Biopsy not done | M. abscessus (culture) | Intralesional corticosteroids, oral penicillin, gentamicin, debridement, clarithromycin 250 mg BID, rifampicin 450 mg daily for nearly 6 months | Full resolution after 6 months with scarring and PIH |
| Yeon et al. | 34/F | Unknown source | Tender nodule over the left mandible after 3.5 months | Suppurative granuloma | M. immunogenum (culture) | Phenoxymethylpenicillin, clindamycin, clarithromycin 500 mg BID for 6 months | Full resolution |
| Saeb-Lima et al. | 45/F | NA | Tender nodules at points of injection 1 week later | Suppurative granuloma with acid-fast positive bacilli | Culture not done | Rifampicin, clarithromycin, azithromycin for 40 days | Full resolution with PIH |
| Ou et al. | 25/F | Unknown source | Painful nodules on cheeks 10 days later | Biopsy not done | M. abscessus (culture) | Azithromycin for 2 weeks, surgical excision, clarithromycin 300 mg for 6 months | Full resolution with dog-ear deformity after excision |
