Research

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16 pages, 3488 KiB

Open AccessArticle

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications

by Abdullah A. Asiri, Ahmad Shaf, Tariq Ali, Muhammad Aamir, Muhammad Irfan, Saeed Alqahtani, Khlood M. Mehdar, Hanan Talal Halawani, Ali H. Alghamdi, Abdullah Fahad A. Alshamrani and Samar M. Alqhtani

Life 2023, 13(7), 1449; https://doi.org/10.3390/life13071449 - 26 Jun 2023

Cited by 9 | Viewed by 9489

Abstract

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their [...] Read more.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Imaging)

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13 pages, 2895 KiB

Open AccessArticle

Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

by Zi Ye, Yogan Jaya Kumar, Fengyan Song, Guanxi Li and Suyu Zhang

Life 2023, 13(4), 1040; https://doi.org/10.3390/life13041040 - 18 Apr 2023

Viewed by 1038

Abstract

Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads [...] Read more.

Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure’s effectiveness. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Imaging)

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14 pages, 4604 KiB

Open AccessArticle

Tumor Area Highlighting Using T2WI, ADC Map, and DWI Sequence Fusion on bpMRI Images for Better Prostate Cancer Diagnosis

by Rossy Vlăduț Teică, Mircea-Sebastian Șerbănescu, Lucian Mihai Florescu and Ioana Andreea Gheonea

Life 2023, 13(4), 910; https://doi.org/10.3390/life13040910 - 30 Mar 2023

Cited by 1 | Viewed by 1720

Abstract

Prostate cancer is the second most common cancer in men worldwide. The results obtained in magnetic resonance imaging examinations are used to decide the indication, type, and location of a prostate biopsy and contribute information about the characterization or aggressiveness of detected cancers, [...] Read more.

Prostate cancer is the second most common cancer in men worldwide. The results obtained in magnetic resonance imaging examinations are used to decide the indication, type, and location of a prostate biopsy and contribute information about the characterization or aggressiveness of detected cancers, including tumor progression over time. This study proposes a method to highlight prostate lesions with a high and very high risk of being malignant by overlaying a T2-weighted image, apparent diffusion coefficient map, and diffusion-weighted image sequences using 204 pairs of slices from 80 examined patients. It was reviewed by two radiologists who segmented suspicious lesions and labeled them according to the prostate imaging-reporting and data system (PI-RADS) score. Both radiologists found the algorithm to be useful as a “first opinion”, and they gave an average score on the quality of the highlight of 9.2 and 9.3, with an agreement of 0.96. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Imaging)

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14 pages, 3051 KiB

Open AccessArticle

The Utility of Multimodal Imaging and Artificial Intelligence Algorithms for Overlying Two Volumes in the Decision Chain for the Treatment of Complex Pathologies in Interventional Neuroradiology—A Case Series Study

by Bogdan Valeriu Popa, Aurelian Costin Minoiu, Catalin Juratu, Cristina Fulgoi, Dragos Trifan, Adrian Tutelca, Dana Crisinescu, Dan Adrian Popica, Cristian Mihalea and Horia Ples

Life 2023, 13(3), 784; https://doi.org/10.3390/life13030784 - 14 Mar 2023

Viewed by 1345

Abstract

3D rotational angiography is now increasingly used in routine neuroendovascular procedures––in particular, for situations where the analysis of two overlayed sets of volume imaging proves useful for planning the treatment strategy or for confirming the optimal apposition of the intravascular devices used. The [...] Read more.

3D rotational angiography is now increasingly used in routine neuroendovascular procedures––in particular, for situations where the analysis of two overlayed sets of volume imaging proves useful for planning the treatment strategy or for confirming the optimal apposition of the intravascular devices used. The aim of this study is to identify and describe the decision algorithm for which the overlay function of 3D rotational angiography volumes, high-resolution contrast-enhanced flat panel detector CT adapted for intravascular devices (VasoCT/DynaCT), non-enhanced flat detector C-arm volume acquisition functionality integrated with the angiography equipment (XperCT/DynaCT), and isovolumetric MRI volumes were all used in treatments performed in a series of 29 patients. Two superposed 3DRA volumes were used in the treatment aneurysms located at the junction of two vascular territories and for arteriovenous malformations with compartments fed from different vascular territories. The superposition function of a preoperatively acquired 3DRA volume and a postoperatively acquired VasoCT volume provides accurate information about the apposition of neuroendovascular endoprostheses used in the treatment of aneurysms. The automatic overlay function generated by the 3D workstation is particularly useful, but in about 50% of cases it requires manual operator-dependent correction, requiring a certain level of experience. In our experience, multimodal imaging brings an important benefit, both in the treatment decision algorithm and in the assessment of neuroendovascular treatment efficacy. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Imaging)

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19 pages, 5885 KiB

Open AccessArticle

Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations

by Mădălin Mămuleanu, Cristiana Marinela Urhuț, Larisa Daniela Săndulescu, Constantin Kamal, Ana-Maria Pătrașcu, Alin Gabriel Ionescu, Mircea-Sebastian Șerbănescu and Costin Teodor Streba

Life 2022, 12(11), 1877; https://doi.org/10.3390/life12111877 - 14 Nov 2022

Cited by 3 | Viewed by 1697

Abstract

Background: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. Methods: The aim of the study was to build a deep learning model for image [...] Read more.

Background: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. Methods: The aim of the study was to build a deep learning model for image segmentation in ultrasound video investigations. The dataset used in the study was provided by the University of Medicine and Pharmacy Craiova, Romania and contained 50 video examinations from 49 patients. The mean age of the patients in the cohort was 69.57. Regarding presence of a subjacent liver disease, 36.73% had liver cirrhosis and 16.32% had chronic viral hepatitis (5 patients: chronic hepatitis C and 3 patients: chronic hepatitis B). Frames were extracted and cropped from each examination and an expert gastroenterologist labelled the lesions in each frame. After labelling, the labels were exported as binary images. A deep learning segmentation model (U-Net) was trained with focal Tversky loss as a loss function. Two models were obtained with two different sets of parameters for the loss function. The performance metrics observed were intersection over union and recall and precision. Results: Analyzing the intersection over union metric, the first segmentation model obtained performed better compared to the second model: 0.8392 (model 1) vs. 0.7990 (model 2). The inference time for both models was between 32.15 milliseconds and 77.59 milliseconds. Conclusions: Two segmentation models were obtained in the study. The models performed similarly during training and validation. However, one model was trained to focus on hard-to-predict labels. The proposed segmentation models can represent a first step in automatically extracting time-intensity curves from CEUS examinations. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Imaging)

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20 pages, 18251 KiB

Open AccessArticle

Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance

by Dong-Min Son, Yeong-Ah Yoon, Hyuk-Ju Kwon and Sung-Hak Lee

Life 2022, 12(11), 1711; https://doi.org/10.3390/life12111711 - 26 Oct 2022

Cited by 4 | Viewed by 2646

Abstract

Mandibular fractures are the most common fractures in dentistry. Since diagnosing a mandibular fracture is difficult when only panoramic radiographic images are used, most doctors use cone beam computed tomography (CBCT) to identify the patient’s fracture location. In this study, considering the diagnosis [...] Read more.

Mandibular fractures are the most common fractures in dentistry. Since diagnosing a mandibular fracture is difficult when only panoramic radiographic images are used, most doctors use cone beam computed tomography (CBCT) to identify the patient’s fracture location. In this study, considering the diagnosis of mandibular fractures using the combined deep learning technique, YOLO and U-Net were used as auxiliary diagnostic methods to detect the location of mandibular fractures based on panoramic images without CBCT. In a previous study, mandibular fracture diagnosis was performed using YOLO learning; in the detection performance result of the YOLOv4-based mandibular fracture diagnosis module, the precision score was approximately 97%, indicating that there was almost no misdiagnosis. In particular, fractures in the symphysis, body, angle, and ramus tend to be distributed in the middle of the mandible. Owing to the irregular fracture types and overlapping location information, the recall score was approximately 79%, which increased the detection of undiagnosed fractures. In many cases, fractures that are clearly visible to the human eye cannot be grasped. To overcome these shortcomings, the number of undiagnosed fractures can be reduced using a combination of the U-Net and YOLOv4 learning modules. U-Net is advantageous for the segmentation of fractures spread over a wide area because it performs semantic segmentation. Consequently, the undiagnosed case in the middle of the mandible, where YOLO was weak, was somewhat supplemented by the U-Net module. The precision score of the combined module was 95%, similar to that of the previous method, and the recall score improved to 87%, as the number of undiagnosed cases was reduced. Through this study, the performance of a deep learning method that can be used for the diagnosis of the mandibular bone has been improved, and it is anticipated that as an auxiliary diagnostic inspection device, it will assist dentists in making diagnoses. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Imaging)

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16 pages, 1258 KiB

Open AccessArticle

Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images

by Lucian Mihai Florescu, Costin Teodor Streba, Mircea-Sebastian Şerbănescu, Mădălin Mămuleanu, Dan Nicolae Florescu, Rossy Vlăduţ Teică, Raluca Elena Nica and Ioana Andreea Gheonea

Life 2022, 12(7), 958; https://doi.org/10.3390/life12070958 - 26 Jun 2022

Cited by 18 | Viewed by 2300

Abstract

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially [...] Read more.

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Imaging)

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Review

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12 pages, 805 KiB

Open AccessReview

One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging

by Costin Chirica, Danisia Haba, Elena Cojocaru, Andreea Isabela Mazga, Lucian Eva, Bogdan Ionut Dobrovat, Sabina Ioana Chirica, Ioana Stirban, Andreea Rotundu and Maria Magdalena Leon

Life 2023, 13(7), 1561; https://doi.org/10.3390/life13071561 - 14 Jul 2023

Cited by 2 | Viewed by 1736

Abstract

Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma [...] Read more.

Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Imaging)

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