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Transfer learning from T1-weighted to T2-weighted Magnetic resonance sequences for brain image segmentation

CAAI Transactions on Intelligence Technology 2024年第1期9卷 26-39页

作者： Imene Mecheter Maysam Abbod Habib Zaidi abbes amira Brunel University LondonUK Geneva University Hospital GenevaSwitzerland Geneva University GenevaSwitzerland University of Groningen GroningenNetherlands University of Southern Denmark OdenseDenmark University of Sharjah SharjahUAE De Montfort University LeicesterUK

Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human *** segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and *** to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning *** aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation *** the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer ***,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation *** novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and *** segmentation results are evaluated using 14 clinical 3D brain MR and CT *** results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±*** T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.

关键词： computer vision convolution image segmentation learning(artificial intelligence)

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Complete pathologic response after chemoradiotherapy in a patient with rectal squamous cell carcinoma： a case report

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Cancer Biology & Medicine 2017年第4期14卷 414-417页

作者： Zaied Sonia Daldoul amira Bhiri Hanene Ammar Nouha Khechine Wiem Toumi Omar abbes Ibtissem Ben Salem Amina Njima Manel Mhabrech Houda Department of Medical Oncology Fattouma Bourguiba University Hospital of Monastir Monastir 5000 Tunisia Department of Surgery Fattouma Bourguiba University Hospital of Monastir Monastir 5000 Tunisia Department of Radiology El Omrane University Hospital of Monastir Monastir 5000 Tunisia Department of Pathological Anatomy Fattouma Bourguiba University Hospital of Monastir Monastir 5000 Tunisia

Squamous cell carcinoma(SCC)of the rectum is a rare disease.A 59-year-old man presented with SCC of the middle rectum located 10 cm from the *** stage of the tumor was revealed to be T3N+*** patient received a combined treatment with cisplatin and fluorouracil in concomitance with external radiation *** then underwent an anterior resection of the *** postoperative histopathological findings classified the tumor as yp T0N0 with cancer-free margins and lymph *** of SCC remains very challenging,and the acquisition of more consistent data is needed.

关键词： Squamous cell carcinoma rectal neoplasm surgery colonoscopy

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