Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation

This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Frechet Inception Distance (FID) score of 78.47, compared to scores above 83.79) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6694 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Conditional Diffusion Models for Semantic 3D Medical Image Synthesis

This paper introduces Med-DDPM, an innovative solution using diffusion models for semantic 3D medical image synthesis, addressing the prevalent issues in medical imaging such as data scarcity, inconsistent acquisition methods, and privacy concerns. Experimental evidence illustrates that diffusion models surpass Generative Adversarial Networks (GANs) in stability and performance, generating high-quality, realistic 3D medical images. The distinct feature of Med-DDPM is its use of semantic conditioning for the diffusion model in 3D image synthesis. By controlling the generation process through pixel-level mask labels, it facilitates the creation of realistic medical images. Empirical evaluations underscore the superior performance of Med-DDPM over GAN techniques in metrics such as accuracy, stability, and versatility. Furthermore, Med-DDPM outperforms traditional augmentation techniques and synthetic GAN images in enhancing the accuracy of segmentation models. It addresses challenges such as insufficient datasets, lack of annotated data, and class imbalance. Noting the limitations of the Frechet inception distance (FID) metric, we introduce a histogram-equalized FID metric for effective performance evaluation. In summary, Med-DDPM, by utilizing diffusion models, signifies a crucial step forward in the domain of high-resolution semantic 3D medical image synthesis, transcending the limitations of GANs and data constraints. This method paves the way for a promising solution in medical imaging, primarily for data augmentation and anonymization, thus contributing significantly to the field

APPLIED ORTHOGONAL EXPERIMENT DESIGN FOR THE OPTIMUM MICROWAVE-ASSISTED EXTRACTION CONDITIONS OF POLYSACCHARIDES FROM RHODIOLAE RADIX

An experiment on polysaccharides from Rhodiolae Radix (PRR) extraction was carried out using microwave-assisted extraction (MAE) method with an objective to establish the optimum MAE conditions of PRR. Single factor experiments were performed to determine the appropriate range of extraction conditions, and the optimum conditions were obtained by using orthogonal experiment design. The results showed that the optimum MAE conditions of PRR were as follows: solid-liquid ratio of 1:45 g/mL, irradiation power of 480 W, and irradiation time of 8 min, while extraction yield of PRR was 3.24 %

Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery

Semantic segmentation of medical images with deep learning models is rapidly developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset, demonstrating the strengths and weaknesses of these algorithms in a fairly practical scenario. In particular, we compared the model performances with respect to their sampling method, model architecture, and the choice of loss functions, identifying the suitable settings for their applications and shedding light on the possible improvements

Comparison of doxycycline and benzathine penicillin G for the treatment of early syphilis

Doxycycline is the preferred recommended second-line treatment for the treatment of early syphilis. Recent reports showed a declining efficacy trend of doxycycline in treatment of early syphilis. The aim of our study was to assess the serological response to the treatment for early syphilis with doxycycline compared with benzathine penicillin G and evaluate whether doxycycline is still an effective agent for the treatment of early syphilis. A record-based retrospective study was conducted. Patients were diagnosed with early syphilis in an sexually transmitted disease (STD) clinic from January 1, 2008 to December 31, 2014. They were treated with a single dose of benzathine penicillin G 2.4MU or oral doxycycline 100 mg twice daily for 14 days. Pearson’s chi-squared test was used for data analysis. 601 cases were included in the final study sample: 105 (17.5%) patients received a 14-day course of doxycycline (doxycycline group), and 496 (82.5%) patients received single-dose benzathine penicillin G (BPG group). The serological responses at 6 months and 12 months after treatment were compared. No statistically significant differences were found between the two groups at 6 months (69.52% vs. 75.00%, P=0.245), and at 12 months (92.38% vs. 96.17%, P=0.115). Doxycycline is still an effective agent for the treatment of early syphilis. </p

Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

<p>Abstract</p> <p>Background</p> <p>In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.</p> <p>This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain.</p> <p>Methods</p> <p>The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system.</p> <p>Results</p> <p>The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.</p> <p>Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related.</p> <p>Conclusions</p> <p>Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.</p

Image Evaluation on the Effort and Effect of Craniectomy

顱骨切除術是最常見的腦部手術之一，主要是希望藉由移除部份的顱骨，使得因外傷或中風而腫脹的腦部有膨脹的空間，避免因為顱內壓上而導致腦部的進一步傷害。雖然有很多研究認為顱骨切除術對改善預後非常有效，卻也有一些研究認為此手術無明顯效果。 有鑑於此，我們認為應該有量化的指標在影像學上評估顱骨切除術的成果與效果。我們提出顱骨切除成果的指標為顱骨切除體積與顱骨切除表面積，兩者也可說是減壓努力的指標。除了由電腦計算之外，我們也可以利用簡單的公式加以估算顱骨切除的體積與面積。由於相對容易，我們認為有關顱骨切除術的研究皆應該將切除的體積或面積當作常規的指標。 我們提出的減壓效果指標包括：中線回返量，顱容積增量和腦體積增量。這些指標與減壓努力指標有正相關，與時間也有相關性。除了由電腦計算之外，中線回返量較容易由影像上直接量出。 我們發現這些指標和許多臨床因素有相關性。這些減壓努力和效果指標和術後顱內壓和神經學結果也有關聯，值得進一步探討。Craniectomy is one of the most common cranial surgeries. The object of this procedure is basically to provide an extra space for the swollen brain by removing part of the skull, and therefore reducing the intracranial pressure and avoiding secondary injuries to the brain. The most common indications of craniectomy are severe head injuries and large infarcts. Many studies showed positive effects of craniectomy for neurological outcomes, but there were some studies showing no effect. We propose some quantitative image parameters for evaluation of decompressive efforts and decompressive effects. The former includes the craniectomy volume and surface area. In addition to the computerized measurement, the craniectomy volume and area can also be estimated by simple formulas. It is so simple that the estimation of craniectomy volume or area should be considered a routine in every craniectomy study. The parameters for decompressive effects include the midline return, potential cranial capacity increment, and actual brain volume increment. These parameters also correlate with decompressive efforts and are actually functions of time. In addition to the computerized measurement, the midline return can be readily measured manually. We observed some correlation between these parameters and many clinical variables. They also correlated with the postoperative intracranial pressure and neurological outcome. Further studies should be done to clarify the clinical significance

Study on the Dissolution and Precipitation Behavior of Self-Designed (NbTi)C Nanoparticles Addition in 1045 Steel

Self-designed (NbTi)C nanoparticles were obtained by mechanical alloying, predispersed in Fe powder, and then added to 1045 steel to obtain modified cast steels. The microstructure of cast steels was investigated by an optical microscope, scanning electron microscope, X-ray diffraction, and a transmission electron microscope. The results showed that (NbTi)C particles can be added to steels and occur in the following forms: original ellipsoidal morphology nanoparticles with uniform dispersion in the matrix, cuboidal nanoparticles in the grain, and microparticles in the grain boundary. Calculations by Thermo-Calc software and solubility formula show that cuboidal (NbTi)C nanoparticles were precipitated in the grain, while the (NbTi)C microparticles were formed by eutectic transformation. The results of the tensile strength of steels show that the strength of modified steels increased and then declined with the increase in the addition amount. When the addition amount was 0.16 wt.%, the modified steel obtained the maximum tensile strength of 759.0 MPa, which is an increase of 52% compared with to that with no addition. The hardness of the modified steel increased with the addition of (NbTi)C nanoparticles. The performance increase was mainly related to grain refinement and the particle strengthening of (NbTi)C nanoparticles, and the performance degradation was related to the increase in eutectic (NbTi)C