The spatial resolution and some other properties of five medical imaging tools are listed and compared in Table 1. Each instrument has different temporal and spatial resolutions, and the corresponding use scenarios are also different.

Magnetic resonance imaging (MRI) is an important non-invasive imaging method for medical diagnosis based on the principle of nuclear magnetic resonance (Hespel and Cole, 2018). Protons precession in a strong magnetic field. When the frequency of the electromagnetic wave emitted to the proton is equal to the precession frequency, the proton will resonate and produce a transition. When the external energy pulse disappears, the proton will return from the ordered high-energy state to the disordered low-energy state and release radio waves, which can be received by the receiving coil and fall into the radio frequency range. The released energy follows the exponential decay form (Yousaf et al., 2018). The time used to release energy is called relaxation time. The relaxation time of different biological tissues is different, which is also the core principle of nuclear magnetic resonance imaging. The field strength of MRI equipment used in the clinic is mainly 1.5T and 3T. Equipment with higher field strength has a higher signal-to-noise ratio and contrast. The uMR Jupiter 5.0T has been developed for clinical whole-body scanning imaging. It shows better image quality and performance in detecting tiny details in various organs, as well as provides more precise quantitative analysis (Zhang et al., 2022). MRI is often used in the brain, blood vessels, spinal cord, abdominal and pelvic organs, musculoskeletal and so on, which can be used to study brain tumors, Parkinson’s disease, mental diseases, and so on (Meijer and Goraj, 2014; Liu et al., 2020a). MRI signal needs spatial positioning (Hamilton et al., 2017), which takes longer time compared with other imaging methods. Still, it will not cause damage to human body or imaging tissue due to the use of non-ionizing electromagnetic radiation. Perfusion MRI was studied to evaluate perfusion parameters at the capillary level. It can be divided into two categories: using and not using exogenetic contrast agents (Jahng et al., 2014). Magnetic resonance spectroscopy (MRS) is a non-invasive metabolic imaging technology based on the same principle as MRI. MRS is most commonly obtained by ¹H. In addition, it can also be obtained by ¹³C, ³¹P, and other nuclei (Speyer and Baleja, 2021). Single voxel spectroscopy (SVS) is the most commonly used and easily obtained MRS technology (Zhang et al., 2018), which is limited to receiving signals from a single voxel. Multi-voxel chemical shift imaging (CSI) techniques, including 2D and 3D CSI, have a larger coverage area, which can be displayed as a single spectrum, a spectral map, or a color metabolic image (Zoccatelli et al., 2013). MRS can be used to study the metabolic changes of Alzheimer’s disease, amyotrophic lateral sclerosis, brain tumor disease, etc.

MRI has also been used in organoid research. Vascular organoids are imaged to observe whether vascular tissue functions normally. The researchers construct organoid-based orthotopic mouse xenograft models, transplant the endometrial cancer organoid cultured in vitro into the mouse uterus, and observe the tumor growth every week with MRI (Espedal et al., 2021). Researchers have also proposed the possibility of using MRI to study brain organoids (Badai et al., 2020).

Computed tomography (CT) is a commonly used medical image diagnosis method in clinics. It measures the attenuation of x-beams in different projection layers of the human body and finally carries out mathematical reconstruction by computer to synthesize it into three-dimensional images (du Plessis et al., 2018). The initial CT used a translational scanning system. With the advancement of technology, CT scan has gradually evolved into fan beam scanning, electron beam scanning, etc. The number of rows of detectors in CT scans is increasing, and the scanning time is getting shorter and shorter. At present, multi-slice spiral CT scans, such as 64-slice spiral CT have become the mainstream of the market because of their fast-imaging speed and clear imaging. In addition to ordinary scans, CT can also perform enhanced scans by injecting contrast agents to make the lesions appear more clearly. Lung, heart, and blood vessels are suitable for CT examination (Wiant et al., 2009; Cox and Lynch, 2015; Thillai et al., 2021).

Optical coherence tomography (OCT) is a non-invasive, high-resolution optical imaging technique that distinguishes different tissues by analyzing the difference between the incident signal and the received signal, taking advantage of the different degrees of absorption and scattering of light by different tissues (Podoleanu, 2005; Podoleanu, 2012). OCT is mainly composed of a low coherence light source, Michelson interferometer and photoelectric detection system. According to different signal acquisition units, it can be divided into time domain OCT (TD-OCT) and frequency domain OCT (FD-OCT) (Chaber et al., 2010; Mueller et al., 2010). TD-OCT developed earlier, using a mechanical reference mirror. FD-OCT improves the imaging speed and sensitivity, accelerates the development of OCT, and has become the mainstream of application. FD-OCT can be realized by spectra-domain OCT (SD-OCT) and swept-source OCT (SS-OCT) (Podoleanu, 2012). The spatial resolution of OCT is high, up to several microns, but due to the insufficient penetration of light into the tissue, the imaging depth is between 1 and 3 mm (McCabe and Croce, 2012). Therefore, OCT is suitable for precision medical fields such as intravascular imaging and ophthalmic diseases (Kim et al., 2017). In the field of intravascular imaging, the application scenarios of OCT basically overlap with that of IVUS, but it can provide more detailed intracoronary pathological features (McCabe and Croce, 2012). At the same time, OCT can also be used to evaluate bioabsorbable vascular stents (Okamura et al., 2010; Brugaletta et al., 2012). In the field of ophthalmic diseases, OCT has become the primary imaging method. The initial imaging of the posterior end such as the retina and the optic nerve head, has progressing to the imaging of the anterior segment such as the ocular surface and the anterior segment due to the development of FD-OCT (Fu et al., 2017; Bille, 2019). The development of OCT greatly promotes the research of glaucoma, macular degeneration and other ophthalmic diseases and plays a great auxiliary role in the research of some diseases that may cause retinopathy, such as Alzheimer’s disease and Parkinson’s disease (Cheung et al., 2015; Zou et al., 2020). Compared with CT, MRI, and other imaging technologies commonly used in the clinic, OCT has higher spatial resolution, and higher imaging depth compared with confocal microscope and other microscopic imaging technologies. Therefore, the emergence of OCT makes up for the gap between traditional medical imaging technology and microscopic imaging technology and can provide support for the biomedical field of organoids with thicker tissue.

3D printing has made considerable progress in recent years. 3D printing creates three-dimensional objects by superimposing layers on a two-dimensional plane, which is versatile and customizable. 3D printing has been applied and improved in aerospace, manufacturing, and so on. When 3D printing is combined with medicine, it has evolved further. More and more researchers in the field of biomedical engineering take 3D printing as a transformation tool for biomedical applications. The slice data of medical images can be modeled and printed layer by layer through 3D printing to visualize simulated organs or other structures. This helps researchers study pathology, helps students learn biological structures, and helps patients better understand their own diseases.

3D bioprinting is an application of 3D printing in biomedicine and has become a promising method for tissue engineering and regenerative medicine. Compared with 3D printing, 3D bioprinting uses living cells, biological materials, etc. as “bioinks” to construct artificial multicellular tissues or organs in three dimensions (Dey and Ozbolat, 2020). It can be used to manufacture a three-dimensional framework that has a similar hierarchical structure to living tissues. Currently, popular 3D bioprinting technologies include laser-assisted bioprinting, inkjet bioprinting, and micro-extrusion bioprinting (Zhu et al., 2016). There have been 3D bioprinting studies on skin, bones, liver, nerves, blood vessels, etc. It is expected to produce transplantable biological tissues in the future to meet the demand for organ transplantation (Matai et al., 2020).

Figure 3 shows a typical process of 3D bioprinting; the main steps are imaging, 3D modeling, bioinks selection, bioprinting, post-processing, and applications. It can be seen that there is a close relationship between 3D bioprinting and medical imaging. The first step of 3D bioprinting is to image the tissue or organ to be printed through medical imaging equipment such as CT and MRI (Abdullah and Reed, 2018). The second step is that 3D modeling depends on accurate image segmentation (Squelch, 2018), which can be supported by artificial intelligence. In the final stage of application, medical images can also be used to visually inspect the tissues in vitro or transplanted into the body.

Conventional tissue engineering strategies employ a “top-down” approach in which cells are seeded on biodegradable polymer scaffolds (Nichol and Khademhosseini, 2009), but these approaches often fail to distribute cells rationally and provide a microenvironment for cell survival. The bottom-up modular approach has the advantage of assembling microenvironments, which is more conducive to constructing large-scale biological tissues (Mandrycky et al., 2016). Therefore, 3D printing has brought new impetus to the development of tissue engineering. 3D printing can be used in tissue engineering to rationally assemble multiple types of cells and scaffold materials for tissues. There are already impressive results using 3D printing to build skin, cartilage, blood vessels, etc. for tissue engineering. 3D printing in tissue engineering can be divided into scaffolded and scaffoldless methods. There has been tremendous progress in 3D printing methods with scaffolds. 3D printing can print precise and complex scaffolds for tissue engineering, and it is convenient to introduce computer methods to assist scaffold construction (Zaszczyńska et al., 2021). There are already 3D printed scaffolds for tissue engineering using materials such as metals, ceramics, hydrogels, etc. Wu et al. achieve 3D printing of microvascular networks using a hydrogel layer (Wu et al., 2011). Lee et al. use polycaprolactone (PCL) to create a framework for hepatocyte engineering (Lee et al., 2016). Scaffold-free approach exploits self-assembly processes in developmental biology (Richards et al., 2013). Taniguchi et al. use 3D bioprinting technology to construct a scaffold free trachea with spheroids composed of several types of cells (Taniguchi et al., 2018). Lui et al. demonstrate the enhancement of mechanical stimulation by creating scaffold-free heart tissue from hiPSC-derived cardiomyocyte spheroids (Lui et al., 2021).

3D printing has also been applied in the field of OOC. The microfluidic device of OOC is mainly made by using traditional manufacturing techniques. The more complex the organizational structure, the more complex and time-consuming the microfluidic device of OOC is. Since 3D printing has the advantage that complex spatial structures can be freely designed, it can change the method of fabricating microfluidic devices (Carvalho et al., 2021). The microfluidic device constructed by 3D printing has the characteristics of high accuracy and short time from design to manufacturing (Goldstein et al., 2021). Sochol et al. investigate the potential of using 3D printing to make Kidney-on-a-Chip platforms (Sochol et al., 2016). The liver chip developed by Lee et al. Using 3D printing significantly enhance liver function (Lee and Cho, 2016). The advantages of 3D printing, which is easy to design and implement, will break the technical barriers that exist in the multidisciplinary intersection of OOC, and accelerate the development and innovation of OOC (Knowlton et al., 2016).

MRI can image artificial tissue implanted in the body. Fujihara et al. use MRI to evaluate the maturity of cartilage tissue transplanted into the back of mice (Fujihara et al., 2016). Using small-animal MRI tracking imaging in experimental mice, Apelgren et al. demonstrate that gridded 3D bioprinted tissue allows vascular ingrowth after implantation. Harrington et al. use cellular MRI to continuously image the grafted tissue of artificial blood vessels, realizing the serial study of MRI at the cellular level of tissue engineering (Figure 5A) (Harrington et al., 2011). MRI is also an important tool for imaging tissue engineering scaffolds. Szulc et al. synthesize MnPNH2 for labeling of dECM scaffolds and visualize the scaffolds using MRI, demonstrating the potential for long-term detection of dECM-based tissue engineering (Szulc et al., 2020). Marie et al. use high-resolution 1.5-T MRI to evaluate scaffold structure and detect cell seeding (Poirier-Quinot et al., 2010). Using gadolinium-enhanced MRI to measure negative fixed-charge density in tissue-engineered cartilage in vitro, Miyata et al. assess its relationship to biomechanical properties (Miyata et al., 2010).

The use of micro-CT in tissue engineering has increased significantly, especially in imaging hard tissue. Martin et al. apply micro-CT to tissue engineering scaffolds aimed at bone regeneration, assessing structural changes related to hydration, complementing traditional methods that can only be studied in the dry state (Figure 5B) (Bartoš, 2018). Tim et al. model the bone tissue engineering scaffold based on micro-CT images to evaluate the structural performance (Van Cleynenbreugel et al., 2006). Wang et al. use MICROFIL perfusion and micro-CT for 3D reconstruction of rat blood vessels, helping to analyze the number, diameter, connectivity and other parameters of blood vessels as an objective assessment method for the generation of angiogenesis in tissue-engineered nerves (Wang et al., 2016). Cioffi et al. use micro-CT to construct a 3D model of a cartilage scaffold to help quantify the regulation of cartilage growth by hydrodynamic shearing (Cioffi et al., 2006). Townsend et al. use it to image tracheal tissue engineering to quantify tracheal patency for standardization in future production (Townsend et al., 2020). In addition, Papantoniou et al. use contrast-enhanced nanofocus CT for full-structure imaging of tissue engineering, which has great potential in 3D imaging and quality assessment of tissue engineering (Papantoniou et al., 2014).

OCT is also used in tissue engineering and is especially suitable for imaging tissues with collagen matrix in tissue engineering, such as skin and tendons. Smith et al. use SS-OCT to monitor dermal rehealing of cutaneous wounds (Smith et al., 2010). Yang et al. use PS-OCT to image tissue-engineered tendon (Yang et al., 2010). Chen et al. demonstrate the effect of pulsatile stimulation on the development of engineered blood vessels using OCT for real-time imaging of tissue-engineered vascular grafts (Figure 5C) (Chen et al., 2017). Yang et al. monitor the cell contour and polylactic acid scaffold in tissue engineering by OCT (Yang et al., 2005). Levitz et al. assess the influence of atherosclerotic plaque composition on morphological features of OCT images (Levitz et al., 2007). Ishii et al. use two imaging techniques, OCT and magnetic resonance angiography to assess the patency of tissue engineered biotubes (Ishii et al., 2016). From the above, micro-CT, MRI, and OCT are developing continuously in tissue engineering and artificial tissue imaging, and are increasingly used by researchers.

Among the imaging methods reviewed, MRI has powerful functions and strong soft tissue contrast, which can be applied to most organoid imaging. Perfusion MRI may help provide perfusion parameters of the complex capillary network in artificial microvascular systems currently under study (Figure 6A) (Fleischer et al., 2020). The soft tissue contrast of CT or micro-CT is not as good as that of MRI, and is suitable for positional imaging of tissue that differs in density, such as imaging tumor organoids in tissue with altered density or size. The spatial resolution of OCT is high, but the contrast of soft tissue is relatively general. It is mainly used for eye, skin, and intravascular imaging in clinical practice. The same application field also serves as a reference for the application of OCT in organoids. Lee et al. construct branched tissue-engineered blood vessels to mimic early atherosclerosis (Figure 6B) (Lee et al., 2021). Skin organoids are also emerging as human models for dermatological research (Figure 2B) (Lee and Koehler, 2021). OCT is expected to play an important role in the research of artificial blood vessels and skin organoids.

When using medical imaging to image OOC, it is vital to consider not only the characteristics of the organoid but also the microfluidic chip. Magnetic metals have an adverse effect on MRI, so when MRI or small animal MRI is required for OOC, metal components should be avoided early in microfluidic chip design and during processing. Additionally, if imaging with micro-CT or CT, additional consideration should be given to possible artifacts caused by microfluidic chips, resulting in problems such as image distortion. To remove artifacts, the structure of the OOC can be skillfully laid out in the early stage, and appropriate algorithms including artificial intelligence algorithms can be used in the later stage. It is foreseeable that the application of medical imaging on OOC is not only involved in one of the links but needed to be considered comprehensively and added to the entire design process. The introduction of medical imaging into the field of OOC will help OOC to industrialize and perform large-scale imaging examinations in the future.

MRI has good soft tissue contrast, so the research and analysis of MRI images are very extensive and multifaceted. Research on MRI images has evolved from traditional methods to artificial intelligence methods. This paper mainly reviews the aspects of image reconstruction, image enhancement, object detection, image segmentation, diagnosis and prediction in the order of processing and analysis of MRI. Figure 7 takes brain MRI as an example to show the current research results of artificial intelligence methods.

Object detection is an important link in medical image processing, usually using a square frame to mark and locate areas of interest such as lesions and organs, which is a preprocessing step for further segmentation or classification. Especially for small target lesions, locking the location of the lesions in advance and storing only the surrounding areas for semantic segmentation are conducive to reducing storage consumption and improving the accuracy of segmentation (Kern and Mastmeyer, 2021). It can be divided into detection of 2D MRI slices and 3D MRI image sets. The object detection of 2D images is to feed each slice of the MRI into the training network separately, which can obtain more training data and correspondingly more training volume than 3D object detection (Kern and Mastmeyer, 2021). But the disadvantage is that the context information will be lost. The current development trend is 3D object detection, which can make more full use of context information to improve the detection accuracy. In the research field of independent detection of 2D MRI, Zhou et al. use the transfer learning method and a special similarity function to pre-train the CNN in the pre-prepared data, and realize the use of unlabeled images of the lumbar spine to detect the vertebral position during the training process (Zhou et al., 2018). Zhang et al. use the classic Faster-RCNN (Figure 8B) to detect brain cancer metastasis, which has superior performance and application prospects (Zhang et al., 2020a). In the field of 3D detection, Alkadi et al. use 3D sliding window for prostate cancer detection (Alkadi et al., 2018). Qi et al. use a 3D CNN to detect cerebral microbleeds (CMBs), achieving a high sensitivity of 93.16% (Qi et al., 2016). Mohammed et al. use YOLO and 3D CNN to detect CMBs.

Image segmentation aims to describe the contour of organs, tissue structures, and lesions as accurately as possible or identify the voxel set in them. Since MRI is good at depicting human soft tissues, especially the human brain, segmentation of MRI has attracted great interest from researchers. Meanwhile, the noise and artifacts of MRI images have brought challenges to segmentation (Despotović et al., 2015). Fully Convolutional Network (FCN) is the pioneer of currently popular medical image segmentation based on convolutional neural networks (CNN) (Shelhamer et al., 2017). In the multi-atlas and diffeomorphism-based encoding block, both MRI intensity profiles and expert priors from deformed atlases were encoded and fed to the proposed FCN. The MRI intensity and the expert priors from the deformation map are coded and input, and the adaptive size patches are used at the same time (Wu and Tang, 2021). The Mask RCNN framework also has a good performance in medical image segmentation. Zhang et al. use Mask RCNN to achieve tumor segmentation for breast MRI, achieving an accuracy of 0.75 on the test set (Zhang et al., 2020b) The U-Net architecture proposed by ronneberger et al. has the structure of u-encoder and decoder and the unique skip connection to help compensate for the information loss in the down sampling process (Figure 8C) (Ronneberger et al., U-Net). The design performs well in medical image segmentation and is widely used by researchers as the basic network for research. V-Net expands the segmentation method of U-Net from two-dimensional images to three-dimensional images. It uses a new loss function called dice coefficient, and replaces the pooling layer in the U-Net architecture with a convolutional layer to achieve fast (1s) and accurate (approximately 87%) volume segmentation of prostate MRI images (Milletari et al., V-Net). UNet++ (Figure 8D) is a collection of U-Net with different depths and redesigned the skip-connection in U-Net. Segmentation experiments are carried out on 6 different biomedical images, including 2D and 3D applications for brain tumor MRI image segmentation (Zhou et al., 2020). The proposal of nnUnet verify the rationality of the original U-Net framework. It only needs to adaptively set the data fingerprint and pipeline fingerprint according to different tasks. The result has won the 2020 MRI-based BraTS brain tumor segmentation competition (Isensee et al., 2021). At the same time, some researchers use recurrent neural networks (RNN) for segmentation. Rudra et al. use recurrent fully-convolutional network (RFCN) for multi-layer MRI cardiac segmentation. Recurrent networks can help extract context information from adjacent slices and improve segmentation quality (Poudel, Lamata, Montana). Andermatt et al. use RNN with multi-dimensional gated recurrent units to segment a brain MRI data set, showing a powerful segmentation ability (Andermatt et al., 2016). Transformer has begun to be applied to the field of medical image segmentation. Peiris et al. propose a U-shaped transformer architecture similar to U-Net, which specially designed the self-attention layer of the encoder and decoder, and showed promising results in MRI brain tumor segmentation (Lehtinen et al., 2018).

CT and MRI are both primary imaging methods in radiology, and the problems to be solved by artificial intelligence are similar. In terms of image reconstruction, Tobias et al. map the filtered back projection algorithm to a neural network to solve the problem of limited-angle tomography, and introduce cone-beam back-projection to overcome the defect that back projection cannot complete the end-to-end network during CT reconstruction (Wurfl et al., 2018). Solomon et al. evaluate a commercial deep learning reconstruction algorithm, and the noise is greatly reduced compared to traditional methods (Solomon et al., 2020). In terms of image enhancement, low-dose CT is often used in order to reduce radiation damage to the human body, but it is accompanied by a reduction in image quality. Li et al. use improved GAN with the Wasserstein distance and a hybrid loss function including sharpness loss, adversarial loss, perceptual loss, etc. for low-dose CT image denoising (Li et al., 2021b). At the same time, low-dose CT also has the problem that the deep learning method is difficult to generalize due to different doses. In order to solve this problem, Shan et al. propose a transfer learning network cascaded by five identical networks, which does not need to be retrained with different doses (Shan et al., 2019). Yao et al. improve convolutional layers and introduce edge detection layers for denoising of micro-CT (Yao et al., 2021). To obtain high-resolution CT images, Zhao et al. construct a network with superior performance using multi-scale attention with multiple branches and information distillation (Zhao et al., 2021). Zhang et al. design a lightweight GAN, construct dense links in all residual blocks in the generator and introduce Wasserstein distance in the loss function to achieve super-resolution (Zhang et al., 2021). In the field of CT image detection, Holbrook et al. use CNN to detect lung nodules in mice based on micro-CT (Holbrook et al., 2021). Lee et al. use three CNN models for coronary artery calcium detection based on CT images. Similarly, the application of image segmentation algorithms in lung CT is more comprehensive, including lung segmentation (Yahyatabar et al., 2020), lung lobe segmentation (Gu et al., 2021), lung parenchyma segmentation (Yoo et al., 2021), lung nodule segmentation (Jain et al., 2021) and other segmentation from larger organs to smaller lesions. Shah et al. test the performance of different deep learning models for COVID-19 detection based on CT images, among which VGG-19 performs the best (Shah et al., 2021). Chen et al. construct a deep learning model with asymmetric convolution based on CT images for predicting the survival rate of non-small cell lung cancer (Chen et al., 2021a).

Currently, many researchers focus on the development of a computer-aided diagnosis (CAD) system for pulmonary nodules on chest CT. The main processes are lung segmentation, lung nodule detection, lung nodule segmentation, lung nodule classification, etc. Tan et al. perform lung segmentation using a GAN network (Figure 9A) (Tan et al., 2021). Cao et al. implement lung nodule detection using a two-stage CNNs (Figure 9B) (Cao et al., 2020). Shi et al. use aggregation U-Net Generative Adversarial Networks for lung nodule segmentation (Figure 9C) (Shi et al., 2020). Zhang et al. use ensemble learners of multiple deep CNNs to classify lung nodules (Figure 9D) (Zhang et al., 2019). More experimental details also include feature extraction and false positive removal for lung nodules. The development of pulmonary nodule CAD system can help clinicians make diagnosis, reduce the workload of doctors, and has good application value and market prospect.

Likewise, artificial intelligence has begun to develop in the field of OCT images. Since speckle noise can greatly affect the image quality of OCT, researchers seek to denoise images by using CNN and GAN. Wang et al. propose a semi-supervised learning method of GAN with fewer parameters to deal with the overfitting problem caused by too many parameters, and can use less data to complete the training (Figure 10A) (Wang et al., 2021b). Zhou et al. use CycleGAN to unify the style of images captured by different OCT instruments, and use conditional GAN for denoising (Zhou et al., 2022).

In the field of OCT intravascular imaging, researchers have applied artificial intelligence to assist in the diagnosis of atherosclerosis. Abhijit et al. propose a distribution preserving autoencoder based neural network for plaque detection in blood. To adapt to the spatiotemporal uncertainty of OCT speckle images, the model learns the representation in the data while preserving the statistical distribution of the data, and a newly proposed LogLoss function is used for error evaluation (Figure 10B) (Roy et al., 2016). To further identify vulnerable plaques, they propose to use a bag of random forests to learn tissue photon interactions (Roy et al., 2015). Asaoka et al. use deep learning to diagnose early-onset glaucoma based on macular OCT images, and use transfer learning to deal with differences in images acquired by different OCT devices (Asaoka et al., 2019). Thomas et al. construct a neural network of encoder and decoder, and complete the classification of fluid and non-fluid regions by semantic segmentation of OCT images, realizing the detection and quantification of macular fluid IRC and SRF, which is convenient for the diagnosis of exudative macular disease (Schlegl et al., 2018).

Research on automated segmentation of retinal OCT images contributes to the diagnosis of retinopathy-related diseases. Li et al. use an improved Xception65 to extract feature information, pass it into the spatial pyramid module to obtain multi-scale information, and finally used an encoder-decoder structure for retinal layer segmentation (Figure 10C) (Li et al., 2020b). Yang et al. achieve retinal layer segmentation with choroidal neovascularization, which responds to retinal morphological changes by introducing a self-attention mechanism (Yang et al., 2020). In (Roy et al., 2017), an end-to-end full CNN with encoder and decoder is constructed, realizing simultaneous segmentation of multiple retinal and fluid pockets to aid in the diagnosis of diabetic retinopathy. Artificial intelligence methods can also be combined with other methods for segmentation, Fang et al. conduct probability mapping on nine retinal layer boundaries through CNN and describe the boundary using the graph search method (Fang et al., 2017). In order to improve the segmentation accuracy, Srinivasan et al. first use sparsity-based image denoising, and then combine graph theory, dynamic programming and SVM to segment the ten-layer boundary of the mouse retina (Srinivasan et al., 2014).

Recently, researchers have begun to use optical coherence tomography angiography (OCTA) images to study retinal blood vessel segmentation. Compared with the more commonly used color fundus imaging techniques, OCTA can present subtle microvessels. Methods for vessel segmentation using deep learning can be roughly divided into two categories. The first category is to use multiple deep learning networks to refine the segmentation results. Ma et al. create a dataset ROSE containing 229 annotated OCTA images and propose a two-stage vessel segmentation network (OCTA-Net), where the coarse stage module is used to generate preliminary confidence maps, and the fine stage is to optimize vessel shape (Figure 10D) (Ma et al., 2021). The second is to enhance the ability of a single network to extract features. Mou et al. use U-Net as the basis and combined with self-attention mechanism to build a channel and spatial attention network, which can process various types of images from corneal confocal microscopy and OCTA (Mou et al., 2019). Li et al. propose an image projection network (IPN). The network architecture uses three-dimensional convolution and unidirectional pooling to achieve 3D-to-2D retinal vessel segmentation and foveal avascularzone segmentation (Li et al., 2020c). In (Liu et al., 2020b), unsupervised OCTA retinal vessel segmentation is proposed using encoders constructed from the same regions of different devices (Liu et al., 2020b).

The development of artificial intelligence on OOC mainly focuses on the analysis of organoid images. Our team build a fully automated tumor spheres analysis system (Figure 11A) that integrates automatic identification, autofocus, and a CNN algorithm based on improved U-Net for accurate tumor boundary detection (Figure 11B). Moreover, two comprehensive parameters—the excess perimeter index and the multiscale entropy index are developed to analyze tumor invasion (Chen et al., 2021b). Bian et al. develop a deep learning model for detection and tracking of high-throughput organoid images. It is mainly implemented in two steps. The first step is to detect the organoids in the collected images of all periods, and the second step is to perform feature extraction on the detected organoids, and calculate the similarity of adjacent periods of organoids for tracking (Figure 11C) (Bian et al., 2021). Kegeles et al. use deep learning algorithms for retinal organoid differentiation, specifically using transfer learning to train a CNN for feature extraction and sample classification (Kegeles et al., 2020). Kong et al. use machine learning methods in colorectal and bladder organoid models to predict the efficacy of anti-cancer drugs in patients (Kong et al., 2020). In addition, researchers have improved deep learning methods for characterizing organoid models using augmented loss functions based on previous studies (Winkelmaier and Parvin, 2021). The development of organoids and OOC is unstoppable, and the application of artificial intelligence methods will undoubtedly bring greater vitality and impetus to the development of this field.

1) A close combination between OOC or organoid and image-guided radiotherapy may provide extra benefits for the treatment of related diseases, especially in the field of oncology, which requires a more precise localization and efficient workflow. This challenge was overcome by a newly-designed integrated CT linear accelerator (linac) uRT-linac 506c, by achieving a diagnostic-quality visualization of anatomical structures and a seamless workflow (Yu et al., 2021). Artificial intelligence algorithm is also applied to the dose prediction of intensity-modulated radiotherapy plan generating to simply the clinical trial (Sun et al., 2022).

Imaging of tissue-engineered artificial tissues and OOCs is in the ascendant. Admittedly, there are limited works utilizing medical imaging tools for tissue engineering and OOC researches. However, with the increase in the application of 3D tissue models and OOCs in drug discovery, environmental protection, and personalized medicine, we believe, in the very near future, the use of medical imaging technology to image micro-organs and use AI for analysis could be a mainstream methodology for organoid and OOC imaging. This paper reviews the research on medical imaging, artificial intelligence especially deep learning application and 3D tissue construction technology, as well as the combination of the two, which will provide relevant biomedical engineering researchers with effective imaging methods for different organoids, and lead to a more rapid development of research in this field.