Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays

Due to the necessity for precise treatment planning, the use of panoramic X-rays to identify different dental diseases has tremendously increased. Although numerous ML models have been developed for the interpretation of panoramic X-rays, there has not been an end-to-end model developed that can identify problematic teeth with dental enumeration and associated diagnoses at the same time. To develop such a model, we structure the three distinct types of annotated data hierarchically following the FDI system, the first labeled with only quadrant, the second labeled with quadrant-enumeration, and the third fully labeled with quadrant-enumeration-diagnosis. To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. Specifically, to take advantage of the hierarchically annotated data, our method utilizes a novel noisy box manipulation technique by adapting the denoising process in the diffusion network with the inference from the previously trained model in hierarchical order. We also utilize a multi-label object detection method to learn efficiently from partial annotations and to give all the needed information about each abnormal tooth for treatment planning. Experimental results show that our method significantly outperforms state-of-the-art object detection methods, including RetinaNet, Faster R-CNN, DETR, and DiffusionDet for the analysis of panoramic X-rays, demonstrating the great potential of our method for hierarchically and partially annotated datasets. The code and the data are available at: https://github.com/ibrahimethemhamamci/HierarchicalDet.Comment: MICCAI 202

DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays

Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential to aid in the analysis of these X-rays, thereby improving the accuracy of dental diagnoses and treatment plans. Nevertheless, designing automated algorithms for this purpose poses significant challenges, mainly due to the scarcity of annotated data and variations in anatomical structure. To address these issues, the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. This challenge aims to promote the development of algorithms for multi-label detection of abnormal teeth, using three types of hierarchically annotated data: partially annotated quadrant data, partially annotated quadrant-enumeration data, and fully annotated quadrant-enumeration-diagnosis data, inclusive of four different diagnoses. In this paper, we present the results of evaluating participant algorithms on the fully annotated data, additionally investigating performance variation for quadrant, enumeration, and diagnosis labels in the detection of abnormal teeth. The provision of this annotated dataset, alongside the results of this challenge, may lay the groundwork for the creation of AI-powered tools that can offer more precise and efficient diagnosis and treatment planning in the field of dentistry. The evaluation code and datasets can be accessed at https://github.com/ibrahimethemhamamci/DENTEXComment: MICCAI 2023 Challeng

CONSIDERATION OF STEP-OVER RATIO IN OPTIMISATION OF CUTTING PARAMETERS FOR SURFACE ROUGHNESS DURING HIGH SPEED MACHINING

Cutting parameters optimisation studies in milling are usually concerning the main machining parameters as cutting speed, feed rate and depth of cut. The step-over ratio is hardly ever considered in optimisation of milling process. The main aim of this study is to discover the impact degree of cutting parameters, especially step-over ratio, on surface quality, and also to realise a satisfactory optimisation with considering it. Accordingly, sample workpieces of AISI 1113 were subjected to end milling at high spindle speeds by using a TiAlN coated flat end mill without using coolant in a CNC vertical machining centre. According to the surface roughness results, the optimum cutting parameters providing the minimum roughness were designated by using the Taguchi method. ANOVA (Analysis of variance) was applied to find the impact degree of parameters on surface quality as statistical way. The results showed that spindle speed, step-over ratio, feed rate and depth of cut affected surface roughness by 32.34, 28.92, 12.38 and 4.02%, respectively. The step-over ratio has a significant effect in formation of surface roughness almost equal to spindle speed. The average surface roughness was successfully improved up to 76.7% with the optimised machining parameters with considering step-over.Cutting parameters optimisation studies in milling are usually concerning themain machining parameters as cutting speed, feed rate and depth of cut. Thestep-over ratio is hardly ever considered in optimisation of milling process. Themain aim of this study is to discover the impact degree of cutting parameters,especially step-over ratio, on surface quality, and also to realise a satisfactoryoptimisation with considering it. Accordingly, sample workpieces of AISI H13were subjected to end milling at high spindle speeds by using a TiAlN coated flatend mill without using coolant in a CNC vertical machining centre. According tothe surface roughness results, the optimum cutting parameters providing the minimumroughness were designated by using the Taguchi method. ANOVA (Analysisof variance) was applied to find the impact degree of parameters on surfacequality as statistical way. The results showed that spindle speed, step-over ratio,feed rate and depth of cut affected surface roughness by 32.34, 28.92, 12.38 and4.02%, respectively. The step-over ratio has a significant effect in formation ofsurface roughness almost equal to spindle speed. The average surface roughnesswas successfully improved up to 76.7% with the optimised machining parameterswith considering step-over.</p

INVESTIGATION OF THE METALLOGRAPHIC AND MECHANICALPROPERTIES OF Fe/B4C-B COMPOSITES PRODUCED AT DIFFERENTSINTERING TEMPERATURES

Bu &ccedil;alışmada, bor (B) ve farklı takviye oranlarında bor&nbsp;karb&uuml;r (B4C) ile takviyelendirilmiş demir (Fe) matrisli&nbsp;kompozit malzemelerin farklı sıcaklıklarda sinterleme&nbsp;sonrasında mikroyapı, sertlik ve porozite &ouml;zellikleri&nbsp;araştırılmıştır. %10 B ve d&ouml;rt farklı hacim oranında (%5-10-20-30) B4C i&ccedil;eren kompozit numuneler toz metal&uuml;rjisiy&ouml;ntemiyle sıcak preste &uuml;retilmiş ve koruyucu atmosfer&nbsp;altında &uuml;&ccedil; farklı sıcaklık değerinde (1000-1150-1300&deg;C) bir&nbsp;saat s&uuml;reyle sinterlenmiştir. Metalografik muayeneleriyapılan numunelerin daha sonra mikro sertlik değerleri&nbsp;belirlenmiştir. Sinterleme sıcaklığının artmasıyla sertlik&nbsp;değerleri artmıştır. En y&uuml;ksek sertlik değeri 1300&deg;Csinterleme sonunda %20 B4C takviye oranına sahip&nbsp;numunede &ouml;l&ccedil;&uuml;lm&uuml;şt&uuml;r. Aynı takviye oranına sahip&nbsp;(%10B,%10B4C) kompozitlerden B4C takviyeli numunedesertlik daha y&uuml;ksek &ouml;l&ccedil;&uuml;lm&uuml;şt&uuml;r. Numunelerin ger&ccedil;ek&nbsp;yoğunluk değerleri teorik yoğunluk değerinden d&uuml;ş&uuml;k&nbsp;olmuş, B4C oranı arttık&ccedil;a porozite artmış yoğunluk&nbsp;azalmıştır.In this study, microstructure, hardness and porosity&nbsp;properties of iron (Fe) based composite materials,&nbsp;reinforced with boron (B) and different ratios of boron&nbsp;carbide (B4C), were investigated after sintering at different&nbsp;temperatures. Composites, containing 10%B and four&nbsp;different volume fraction (5-10-20-30%) of B4C, wereproduced by powder metallurgy method with hot pressing&nbsp;and sintered at three different temperatures (1000-1150-&nbsp;300&deg;C) under protective atmosphere for one hour.Microhardness values of samples were determined after&nbsp;metallographic examinations. The hardness values were&nbsp;increased with increasing the sintering temperature. Thehighest hardness was measured on 20%B4C-Fe&nbsp;composite at the end of sintering at 1300&deg;C. The hardness&nbsp;of 10%B4C reinforced sample was measured higher than10%B reinforced composite. The actual densities of&nbsp;samples were lower than theoretical density. Porosity was&nbsp;increased and density was decreased with increasing the&nbsp;B4C content.</p

Farklı Sinterleme Sıcaklıklarında Üretilmiş Fe/B4C-B Kompozitlerin Metalografik ve Mekanik Özelliklerinin İncelenmesi

Bu &ccedil;alışmada, bor (B) ve farklı takviye oranlarında bor karb&uuml;r (B4C) ile takviyelendirilmiş demir (Fe) matrisli kompozit malzemelerin farklı sıcaklıklarda sinterleme sonrasında mikroyapı, sertlik ve porozite &ouml;zellikleri araştırılmıştır. %10 B ve d&ouml;rt farklı hacim oranında (%5-10-20-30) B4C i&ccedil;eren kompozit numuneler toz metal&uuml;rjisi y&ouml;ntemiyle sıcak preste &uuml;retilmiş ve koruyucu atmosfer altında &uuml;&ccedil; farklı sıcaklık değerinde (1000-1150-1300&deg;C) bir saat s&uuml;reyle sinterlenmiştir. Metalografik muayeneleri yapılan numunelerin daha sonra mikro sertlik değerleri belirlenmiştir. Sinterleme sıcaklığının artmasıyla sertlik değerleri artmıştır. En y&uuml;ksek sertlik değeri 1300&deg;C sinterleme sonunda %20 B4C takviye oranına sahip numunede &ouml;l&ccedil;&uuml;lm&uuml;şt&uuml;r. Aynı takviye oranına sahip (%10B,%10B4C) kompozitlerden B4C takviyeli numunede sertlik daha y&uuml;ksek &ouml;l&ccedil;&uuml;lm&uuml;şt&uuml;r. Numunelerin ger&ccedil;ek yoğunluk değerleri teorik yoğunluk değerinden d&uuml;ş&uuml;k olmuş, B4C oranı arttık&ccedil;a porozite artmış yoğunluk azalmıştır.&nbsp;In this study, microstructure, hardness and porosity properties of iron (Fe) based composite materials, reinforced with boron (B) and different ratios of boron carbide (B4C), were investigated after sintering at different temperatures. Composites, containing 10%B and four different volume fraction (5-10-20-30%) of B4C, were produced by powder metallurgy method with hot pressing and sintered at three different temperatures (1000-1150-1300&deg;C) under protective atmosphere for one hour. Microhardness values of samples were determined after metallographic examinations. The hardness values were increased with increasing the sintering temperature. The highest hardness was measured on 20%B4C-Fe composite at the end of sintering at 1300&deg;C. The hardness of 10%B4C reinforced sample was measured higher than 10%B reinforced composite. The actual densities of samples were lower than theoretical density. Porosity was increased and density was decreased with increasing the B4C content. &nbsp;</p