Conventional radiography is the widely used imaging technology in diagnosing, monitoring, and prognosticating musculoskeletal (MSK) diseases because of its easy availability, versatility, and cost-effectiveness. Bone overlaps are prevalent in conventional radiographs, and can impede the accurate assessment of bone characteristics by radiologists or algorithms, posing significant challenges to conventional clinical diagnosis and computer-aided diagnosis. This work initiated the study of a challenging scenario - bone layer separation in conventional radiographs, in which separate overlapped bone regions enable the independent assessment of the bone characteristics of each bone layer and lay the groundwork for MSK disease diagnosis and its automation. This work proposed a Bone Layer Separation GAN (BLS-GAN) framework that can produce high-quality bone layer images with reasonable bone characteristics and texture. This framework introduced a reconstructor based on conventional radiography imaging principles, which achieved efficient reconstruction and mitigates the recurrent calculations and training instability issues caused by soft tissue in the overlapped regions. Additionally, pre-training with synthetic images was implemented to enhance the stability of both the training process and the results. The generated images passed the visual Turing test, and improved performance in downstream tasks. This work affirms the feasibility of extracting bone layer images from conventional radiographs, which holds promise for leveraging layer separation technology to facilitate more comprehensive analytical research in MSK diagnosis, monitoring, and prognosis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint inflammation and progressive structural damage. Joint space width (JSW) is a critical indicator in conventional radiography (CR) for evaluating disease progression, which has become a prominent research topic in computer-aided diagnostic (CAD) systems. However, deep learning-based radiological CAD systems for JSW analysis face significant challenges in data quality, including data imbalance, limited variety, and annotation difficulties. This work introduced a challenging image synthesis scenario and proposed Layer Separation Networks (LSN) to accurately separate the soft tissue layer, the upper bone layer, and the lower bone layer in conventional radiographs of finger joints. Using these layers, the adjustable JSW images can be synthesized to address data quality challenges and achieve ground truth (GT) generation. Experimental results demonstrated that LSN-based synthetic images closely resemble real radiographs, and significantly enhanced the performance in downstream tasks. The code and dataset are available at: https://github.com/pokeblow/LSN.

Accurate identification and delineation of teeth in cone-beam computed tomography (CBCT) images are crucial in the advancement of digital dentistry technology. Teeth exhibit high interclass similarity and often have fuzzy boundaries. In addition, it is difficult to obtain teeth samples due to the time-consuming annotation process. However, existing methods typically fail to incorporate this domain-specific prior information under limited labeled samples, which limits the improvement of segmentation performance. Based on the intrinsic characteristics of the tooth CBCT images, a self-supervised manifold transfer learning network (SMTLNet) is proposed to improve segmentation accuracy. Initially, an object-oriented self-supervised pretraining approach is designed to fully explore valuable image representations from unannotated images, and this helps reduce dependence on labeled samples. Furthermore, a manifold optimization strategy is employed to regularize the segmentation model to separate interclass samples while compacting intraclass neighbors. Finally, to address the issue of blurred tooth boundaries, a multiscale boundary constraint module is developed to extract multiscale boundary-aware features, and more discriminative tooth descriptions can be acquired in this way. The proposed SMTLNet method is evaluated on clinical datasets containing diverse challenging cases (e.g., impacted wisdom teeth, crowded dentition), and it achieves state-of-the-art performance with dice similarity coefficients (DSCs) of 91.8%/89.08% and Jaccard similarities (JSs) of 86.71%/82.87% under full (100%) and limited (20%) training data regimes, respectively. The method maintains anatomical precision with Hausdorff distances (HDs) of 1.41 mm (high-resource) and 2.35 mm (low-resource), demonstrating strong clinical applicability in digital dentistry workflows.