Introduction Medical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible. Methods We present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable. Results and Discussion The uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.

uRP: An integrated research platform for one-stop analysis of medical images

Medical imaging refers to several different technologies that are used to view the human body to diagnose, monitor, or treat medical conditions. It requires significant expertise to efficiently and correctly interpret the images generated by each of these technologies, which among others include radiography, ultrasound, and magnetic resonance imaging. Deep learning and machine learning techniques provide different solutions for medical image interpretation including those associated with detection and diagnosis. Despite the huge success of deep learning algorithms in image analysis, training algorithms to reach human-level performance in these tasks depends on the availability of large amounts of high-quality training data, including high-quality annotations to serve as ground-truth. Different annotation tools have been developed to assist with the annotation process. In this survey, we present the currently available annotation tools for medical imaging, including descriptions of graphical user interfaces (GUI) and supporting instruments. The main contribution of this study is to provide an intensive review of the popular annotation tools and show their successful usage in annotating medical imaging dataset to guide researchers in this area. 

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Towards a better understanding of annotation tools for medical imaging: a survey

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives.

\n Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

The application of artificial intelligence and radiomics in lung cancer

Human cadaveric study has indicated that the metacarpal head (MCH) is intracapsular in location. We hypothesized that exposure to the intra-articular inflammatory milieu in psoriatic arthritis (PsA) will lead to bone loss in the MCH. To compare the bone structure and microstructure in the MCH between patients with PsA and healthy controls by high-resolution peripheral quantitative CT (HR-pQCT), and to ascertain factors associated with bone loss in PsA patients. Sixty-two PsA patients without joint destruction and 62 age-, gender-, and body mass index–matched healthy subjects underwent HR-pQCT imaging of the second and third MCH (MCH 2&3). The number and volume of bone erosion and enthesiophytes, as well as volumetric bone mineral density (vBMD) and microstructure at the MCH 2&3, were recorded. Correlation analysis and multivariable linear regression models were used to determine the association of demographic and disease-specific variables with compromised bone structure and microstructure in PsA. At the MCH 2&3, bone erosion (p = 0.003) and enthesiophyte (p = 0.000) volumes in PsA patients were significantly larger than healthy controls. In PsA patients, older age was associated with a larger erosion and enthesiophyte volume. Concerning the mean vBMD and microstructure at the MCH 2&3, PsA patients had significantly lower mean vBMD (average vBMD − 6.9%, trabecular vBMD − 8.8%, peri-trabecular vBMD − 7.7%, meta-trabecular vBMD − 9.8%), trabecular bone volume fraction (− 8.8%), and trabecular thickness (− 8.1%) compared with control subjects. Multivariable regression analysis revealed that older age and a higher C-reactive protein level were associated with trabecular bone loss. PsA patients had a higher burden of bone damages (erosions and enthesiophytes) and trabecular bone loss compared with healthy control at the MCH. Inflammation contributed to the deterioration in trabecular microstructure in these patients.

Comparison of bone structure and microstructure in the metacarpal heads between patients with psoriatic arthritis and healthy controls: an HR-pQCT study.

The ITK-SNAP procedure is accurate and highly reproducible for the delineation of complex-shaped thoracic tumors on fluorodeoxyglucose PET images.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018171756

Rapid Contour-based Segmentation for 18F-FDG PET Imaging of Lung Tumors by Using ITK-SNAP: Comparison to Expert-based Segmentation

A 59-year-old woman who was receiving immunosuppressive therapy for a recent lung transplantation related to pulmonary Langerhans cell histiocytosis was referred to our PET unit for biological inflammatory syndrome. She had also been monitored for 19 years due to erosive rheumatoid arthritis (RA). Whole-body non-contrastenhanced F–FDG PET/CT was performed. The 3D PET maximal intensity projection (MIP) showed atypical, very intense and extensive F–FDG uptake in the pelvic girdle (Fig. 1a). This vast area of increased metabolic activity mainly included the subcutaneous part of the bottom and posterior region of the two thighs, as observed on the axial PET and fused PET/CT slices (Fig. 1b and c), and appeared as spontaneous, hyperdense and irregular subcutaneous thickening on the corresponding CT slices (Fig. 1d, yellow arrows). These PET/CT findings corresponded to the extensive subcutaneous infiltration by rheumatoid nodules (RNs). The whole-body 3D MIP (Fig. 1a) also showed hypermetabolic RNs in the elbows and several areas of FDG uptake in joints commonly affected by RA [1] including the shoulders, wrists, hips, knees and ankles, which were swollen and painful at the time of PET/CT imaging. RNs are the most common extra-articular manifestation of RA and occur in up to 30% of cases [2]. RNs mainly affect the extensor surfaces of the fingers, forearms, elbows, knees and heels and are correlated with disease severity [3]. Accelerated rheumatoid nodulosis has been reported with the use of several immunosuppressive drugs [4, 5].

18F-FDG PET/CT of extensive rheumatoid nodulosis

Eumycetoma is a chronic tropical fungal disease that infiltrates subcutaneous tissues, mainly in the feet and legs. Standard antifungal drugs are ineffective, and mutilating surgery is common. Morphological imaging is useful for assessing the local extent of the disease before surgery. FDG P

PET/CT and MRI Imaging of a Eumycetoma of the Right Thigh.

Managing decisions of pancreatic neuroendocrine tumors (pNETs) can be challenging because of different clinical presentations and prognosis. A 31-year-old woman with multiple endocrine neoplasia type 1, including a suspicious pNET, was assessed with Ga-DOTATOC and F-FDG PET. A high Ga-DOTATOC uptake was visualized in the entire pNET, whereas a high F-FDG PET uptake was present only in the upper part of the tumor. After surgery, pathology confirmed the pNET with a double component: an upper grade 2 with a Ki67 of 11% with the high F-FDG PET uptake, and a lower grade 1 with a Ki67 of 2%. Combined Ga-DOTATOC/F-FDG PET predicts the grade in heterogeneous pNETs.

Combined 68Ga-DOTATOC and 18F-FDG PET Predicts a Double Component With Different Grade of a Pancreatic Neuroendocrine Tumor in a Patient With Multiple Endocrine Neoplasia Type 1.

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Rapid Contour-based Segmentation for 18F-FDG PET Imaging of Lung Tumors by Using ITK-SNAP: Comparison to Expert-based Segmentation

18F-FDG PET/CT of extensive rheumatoid nodulosis

PET/CT and MRI Imaging of a Eumycetoma of the Right Thigh.

Combined 68Ga-DOTATOC and 18F-FDG PET Predicts a Double Component With Different Grade of a Pancreatic Neuroendocrine Tumor in a Patient With Multiple Endocrine Neoplasia Type 1.