14 research outputs found
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 çalışmada, bor (B) ve farklı takviye oranlarında bor karbür (B4C) ile takviyelendirilmiş demir (Fe) matrisli kompozit malzemelerin farklı sıcaklıklarda sinterleme sonrasında mikroyapı, sertlik ve porozite özellikleri araştırılmıştır. %10 B ve dört farklı hacim oranında (%5-10-20-30) B4C içeren kompozit numuneler toz metalürjisiyöntemiyle sıcak preste üretilmiş ve koruyucu atmosfer altında üç farklı sıcaklık değerinde (1000-1150-1300°C) bir saat süreyle sinterlenmiştir. Metalografik muayeneleriyapılan numunelerin daha sonra mikro sertlik değerleri belirlenmiştir. Sinterleme sıcaklığının artmasıyla sertlik değerleri artmıştır. En yüksek sertlik değeri 1300°Csinterleme sonunda %20 B4C takviye oranına sahip numunede ölçülmüştür. Aynı takviye oranına sahip (%10B,%10B4C) kompozitlerden B4C takviyeli numunedesertlik daha yüksek ölçülmüştür. Numunelerin gerçek yoğunluk değerleri teorik yoğunluk değerinden düşük olmuş, B4C oranı arttıkça porozite artmış yoğunluk azalmıştır.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, wereproduced by powder metallurgy method with hot pressing and sintered at three different temperatures (1000-1150- 300°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. Thehighest hardness was measured on 20%B4C-Fe composite at the end of sintering at 1300°C. The hardness of 10%B4C reinforced sample was measured higher than10%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.</p
Farklı Sinterleme Sıcaklıklarında Üretilmiş Fe/B4C-B Kompozitlerin Metalografik ve Mekanik Özelliklerinin İncelenmesi
Bu çalışmada, bor (B) ve farklı takviye oranlarında bor karbür (B4C) ile takviyelendirilmiş demir (Fe) matrisli kompozit malzemelerin farklı sıcaklıklarda sinterleme sonrasında mikroyapı, sertlik ve porozite özellikleri araştırılmıştır. %10 B ve dört farklı hacim oranında (%5-10-20-30) B4C içeren kompozit numuneler toz metalürjisi yöntemiyle sıcak preste üretilmiş ve koruyucu atmosfer altında üç farklı sıcaklık değerinde (1000-1150-1300°C) bir saat süreyle sinterlenmiştir. Metalografik muayeneleri yapılan numunelerin daha sonra mikro sertlik değerleri belirlenmiştir. Sinterleme sıcaklığının artmasıyla sertlik değerleri artmıştır. En yüksek sertlik değeri 1300°C sinterleme sonunda %20 B4C takviye oranına sahip numunede ölçülmüştür. Aynı takviye oranına sahip (%10B,%10B4C) kompozitlerden B4C takviyeli numunede sertlik daha yüksek ölçülmüştür. Numunelerin gerçek yoğunluk değerleri teorik yoğunluk değerinden düşük olmuş, B4C oranı arttıkça porozite artmış yoğunluk azalmıştır. 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°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°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. </p