The Segment Anything Model (SAM) is a foundational model that has demonstrated impressive results in the field of natural image segmentation. However, its performance remains suboptimal for medical image segmentation, particularly when delineating lesions with irregular shapes and low contrast. This can be attributed to the significant domain gap between medical images and natural images on which SAM was originally trained. In this paper, we propose an adaptation of SAM specifically tailored for lesion segmentation termed LeSAM. LeSAM first learns medical-specific domain knowledge through an efficient adaptation module and integrates it with the general knowledge obtained from the pre-trained SAM. Subsequently, we leverage this merged knowledge to generate lesion masks using a modified mask decoder implemented as a lightweight U-shaped network design. This modification enables better delineation of lesion boundaries while facilitating ease of training. We conduct comprehensive experiments on various lesion segmentation tasks involving different image modalities such as CT scans, MRI scans, ultrasound images, dermoscopic images, and endoscopic images. Our proposed method achieves superior performance compared to previous state-of-the-art methods in 8 out of 12 lesion segmentation tasks while achieving competitive performance in the remaining 4 datasets. Additionally, ablation studies are conducted to validate the effectiveness of our proposed adaptation modules and modified decoder.

Orthopedic spine disease is one of the most common diseases in the clinic. The diagnosis of spinal orthopedic injury is an important basis for the treatment of spinal orthopedic diseases. Due to the complexity of the spine structure, doctors usually need to rely on orthopedic CT image data for accurate diagnosis. In some cases, such as poor areas or in emergency situations, it is difficult for doctors to make accurate diagnoses using only 2D x-ray images due to lack of 3D imaging equipment or time crunch. Therefore, an approach based on 2D x-ray images is needed to solve this problem. In this paper, a novel 3D spine reconstruction technique based on 2D orthogonal x-ray images (3DSRNet) is designed. 3DSRNet uses a generative adversarial network architecture and novel modules to make 3D spine reconstruction more accurate and efficient. Spine reconstruction CNN-transformer framework (SRCT) is employed to effectively integrate local bone surface information and long-range relation spinal structure information. Spine reconstruction texture framework (SRTE) is used to extract spine texture features to enhance the effect of pixel-level reconstruction. Experiments show that 3DSRNet achieves excellent 3D spine reconstruction results on multiple metrics including PSNR (45.4666 dB), SSIM (0.8850), CS (0.7662), MAE (23.6696), MSE (9016.1044), and LPIPS (0.0768).

Medical image denoising faces great challenges. Although deep learning methods have shown great potential, their efficiency is severely affected by millions of trainable parameters. The non-linearity of neural networks also makes them difficult to be understood. Therefore, existing deep learning methods have been sparingly applied to clinical tasks. To this end, we integrate known filtering operators into deep learning and propose a novel Masked Joint Bilateral Filtering (MJBF) via deep image prior for digital X-ray image denoising. Specifically, MJBF consists of a deep image prior generator and an iterative filtering block. The deep image prior generator produces plentiful image priors by a multi-scale fusion network. The generated image priors serve as the guidance for the iterative filtering block, which is utilized for the actual edge-preserving denoising. The iterative filtering block contains three trainable Joint Bilateral Filters (JBFs), each with only 18 trainable parameters. Moreover, a masking strategy is introduced to reduce redundancy and improve the understanding of the proposed network. Experimental results on the ChestX-ray14 dataset and real data show that the proposed MJBF has achieved superior performance in terms of noise suppression and edge preservation. Tests on the portability of the proposed method demonstrate that this denoising modality is simple yet effective, and could have a clinical impact on medical imaging in the future.