Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion.

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated superior performance in predicting interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. Additionally, the model demonstrated satisfactory performance in predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.

Retina-TransNet: A Gradient-Guided Few-Shot Retinal Vessel Segmentation Net.

Due to the high labor cost of physicians, it is difficult to collect a rich amount of manually-labeled medical images for developing learning-based computer-aided diagnosis (CADx) systems or segmentation algorithms. To tackle this issue, we reshape the image segmentation task as an image-to-image (I2I) translation problem and propose a retinal vascular segmentation network, which can achieve good cross-domain generalizability even with a small amount of training data. We devise primarily two components to facilitate this I2I-based segmentation method. The first is the constraints provided by the proposed gradient-vector-flow (GVF) loss, and, the second is a two-stage Unet (2Unet) generator with a skip connection. This configuration makes 2Unet's first-stage play a role similar to conventional Unet, but forces 2Unet's second stage to learn to be a refinement module. Extensive experiments show that by re-casting retinal vessel segmentation as an image-to-image translation problem, our I2I translator-based segmentation subnetwork achieves better cross-domain generalizability than existing segmentation methods. Our model, trained on one dataset, e.g., DRIVE, can produce segmentation results stably on datasets of other domains, e.g., CHASE-DB1, STARE, HRF, and DIARETDB1, even in low-shot circumstances.

Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network.

Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise as the global population ages, requiring prudent action to promptly identify it in clinical settings. The goal of this study was to develop a computer-aided diagnostic (CADx) algorithm, LumbarNet, and to evaluate the efficiency of this model in automatically detecting spondylolisthesis from lumbar X-ray images. Built upon U-Net, feature fusion module (FFM) and collaborating with (i) a P-grade, (ii) a piecewise slope detection (PSD) scheme, and (iii) a dynamic shift (DS), LumbarNet was able to analyze complex structural patterns on lumbar X-ray images, including true lateral, flexion, and extension lateral views. Our results showed that the model achieved a mean intersection over union (mIOU) value of 0.88 in vertebral region segmentation and an accuracy of 88.83% in vertebral slip detection. We conclude that LumbarNet outperformed U-Net, a commonly used method in medical image segmentation, and could serve as a reliable method to identify spondylolisthesis.

DotFAN: A Domain-Transferred Face Augmentation Net.

The performance of a convolutional neural network (CNN) based face recognition model largely relies on the richness of labeled training data. However, it is expensive to collect a training set with large variations of a face identity under different poses and illumination changes, so the diversity of within-class face images becomes a critical issue in practice. In this paper, we propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) that can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets of other domains. Extending from StarGAN's architecture, DotFAN integrates with two additional subnetworks, i.e., face expert model (FEM) and face shape regressor (FSR), for latent facial code control. While FSR aims to extract face attributes, FEM is designed to capture a face identity. With their aid, DotFAN can separately learn facial feature codes and effectively generate face images of various facial attributes while keeping the identity of augmented faces unaltered. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity so that a better face recognition model can be learned from the augmented dataset.

Developing a stereotypical Drosophila brain atlas.

Brain research requires a standardized brain atlas to describe both the variance and invariance in brain anatomy and neuron connectivity. In this study, we propose a system to construct a standardized 3D Drosophila brain atlas by integrating labeled images from different preparations. The 3D fly brain atlas consists of standardized anatomical global and local reference models, e.g., the inner and external brain surfaces and the mushroom body. The averaged global and local reference models are generated by the model averaging procedure, and then the standard Drosophila brain atlas can be compiled by transferring the averaged neuropil models into the averaged brain surface models. The main contribution and novelty of our study is to determine the average 3D brain shape based on the isosurface suggested by the zero-crossings of a 3D accumulative signed distance map. Consequently, in contrast with previous approaches that also aim to construct a stereotypical brain model based on the probability map and a user-specified probability threshold, our method is more robust and thus capable to yield more objective and accurate results. Moreover, the obtained 3D average shape is useful for defining brain coordinate systems and will be able to provide boundary conditions for volume registration methods in the future. This method is distinguishable from those focusing on 2D + Z image volumes because its pipeline is designed to process 3D mesh surface models of Drosophila brains.

Retention of features on a mapped Drosophila brain surface using a Bézier-tube-based surface model averaging technique.

Model averaging is a widely used technique in biomedical applications. Two established model averaging methods, iterative shape averaging (ISA) method and virtual insect brain (VIB) method, have been applied to several organisms to generate average representations of their brain surfaces. However, without sufficient samples, some features of the average Drosophila brain surface obtained using the above methods may disappear or become distorted. To overcome this problem, we propose a Bézier-tube-based surface model averaging strategy. The proposed method first compensates for disparities in position, orientation, and dimension of input surfaces, and then evaluates the average surface by performing shape-based interpolation. Structural features with larger individual disparities are simplified with half-ellipse-shaped Bézier tubes, and are unified according to these tubes to avoid distortion during the averaging process. Experimental results show that the average model yielded by our method could preserve fine features and avoid structural distortions even if only a limit amount of input samples are used. Finally, we qualitatively compare our results with those obtained by ISA and VIB methods by measuring the surface-to-surface distances between input surfaces and the averaged ones. The comparisons show that the proposed method could generate a more representative average surface than both ISA and VIB methods.

Optimal multiresolution blending of confocal microscope images.

Typical mosaicing schemes assume that to-be-combined images are equally informative; thus, the images are processed in a similar manner. However, the new imaging technique for confocal fluorescence images has revealed a problem when two asymmetrically informative biological images are stitched during microscope image mosaicing. The latter process is widely used in biological studies to generate a higher resolution image by combining multiple images taken at different times and angles. To resolve the earlier problem, we propose a multiresolution optimization approach that evaluates the blending coefficients based on the relative importance of the overlapping regions of the to-be-combined image pair. The blending coefficients are the optimal solution obtained by a quadratic programming algorithm with constraints that are enforced by the biological requirements. We demonstrate the efficacy of the proposed approach on several confocal microscope fluorescence images and compare the results with those derived by other methods.