Why shared data should not be acknowledged on the author byline.

We argue that the emerging practice of using the author byline to acknowledge shared data is incompatible with current established standards for academic authorship. Non-author contributors, whether groups or individuals, should not be added to the author list of published papers. Deviation from these principles devalues authorship and raises issues, such as equal treatment of groups and individuals, credit for shared data vs. other shared resources, and ultimately guest authorship. Such dilution of authorship standards is problematic because it can compromise fair evaluations in the scientific community. We briefly discuss viable alternatives for crediting contributors, such as citations of papers describing shared data, reference to dataset publications, inclusion in the Acknowledgments section, or credit of individuals for sharing data in an Appendix, a solution that has been used in academic evaluation.

Value of image fusion using single photon emission computed tomography with integrated low dose computed tomography in comparison with a retrospective voxel-based method in neuroendocrine tumours.

The objective was the evaluation of single photon emission computed tomography (SPECT) with integrated low dose computed tomography (CT) in comparison with a retrospective fusion of SPECT and high-resolution CT and a side-by-side analysis for lesion localisation in patients with neuroendocrine tumours. Twenty-seven patients were examined by multidetector CT. Additionally, as part of somatostatin receptor scintigraphy (SRS), an integrated SPECT-CT was performed. SPECT and CT data were fused using software with a registration algorithm based on normalised mutual information. The reliability of the topographic assignment of lesions in SPECT-CT, retrospective fusion and side-by-side analysis was evaluated by two blinded readers. Two patients were not enrolled in the final analysis because of misregistrations in the retrospective fusion. Eighty-seven foci were included in the analysis. For the anatomical assignment of foci, SPECT-CT and retrospective fusion revealed overall accuracies of 91 and 94% (side-by-side analysis 86%). The correct identification of foci as lymph node manifestations (n=25) was more accurate by retrospective fusion (88%) than from SPECT-CT images (76%) or by side-by-side analysis (60%). Both modalities of image fusion appear to be well suited for the localisation of SRS foci and are superior to side-by-side analysis of non-fused images especially concerning lymph node manifestations.

Shape-based averaging for combination of multiple segmentations.

Combination of multiple segmentations has recently been introduced as an effective method to obtain segmentations that are more accurate than any of the individual input segmentations. This paper introduces a new way to combine multiple segmentations using a novel shape-based averaging method. Individual segmentations are combined based on the signed Euclidean distance maps of the labels in each input segmentation. Compared to label voting, the new combination method produces smoother, more regular output segmentations and avoids fragmentation of contiguous structures. Using publicly available segmented human brain MR images (IBSR database), we perform a quantitative comparison between shape-based averaging and label voting by combining random segmentations with controlled error magnitudes and known ground truth. Shape-based averaging generated combined segmentations that were closer to the ground truth than combinations from label voting for all numbers of input segmentations (up to ten). The relative advantage of shape-based averaging over voting was larger for fewer input segmentations, and larger for greater deviations of the input segmentations from the ground truth. We conclude that shape-based averaging improves the accuracy of combined segmentations, in particular when only a few input segmentations are available and when the quality of the input segmentations is low.

Information fusion in biomedical image analysis: combination of data vs. combination of interpretations.

Information fusion has, in the form of multiple classifier systems, long been a successful tool in pattern recognition applications. It is also becoming increasingly popular in biomedical image analysis, for example in computer-aided diagnosis and in image segmentation. In this paper, we extend the principles of multiple classifier systems by considering information fusion of classifier inputs rather than on their outputs, as is usually done. We introduce the distinction between combination of data (i.e., classifier inputs) vs. combination of interpretations (i.e., classifier outputs). We illustrate the two levels of information fusion using four different biomedical image analysis applications that can be implemented using fusion of either data or interpretations: atlas-based image segmentation, "average image" tissue classification, multi-spectral classification, and deformation-based group morphometry.

[Clinical use of digital retrospective image fusion of CT, MRI, FDG-PET and SPECT -- fields of indications and results]. / Klinischer Einsatz der digitalen retrospektiven Bildfusion von CT, MRT, FDG-PET und SPECT -- Anwendungsgebiete und Ergebnisse.

PURPOSE: To evaluate the feasibility and the clinical benefits of retrospective digital image fusion (PET, SPECT, CT and MRI). MATERIALS AND METHODS: In a prospective study, a total of 273 image fusions were performed and evaluated. The underlying image acquisitions (CT, MRI, SPECT and PET) were performed in a way appropriate for the respective clinical question and anatomical region. Image fusion was executed with a software program developed during this study. The results of the image fusion procedure were evaluated in terms of technical feasibility, clinical objective, and therapeutic impact. RESULTS: The most frequent combinations of modalities were CT/PET (n = 156) and MRI/PET (n = 59), followed by MRI/SPECT (n = 28), CT/SPECT (n = 22) and CT/MRI (n = 8). The clinical questions included following regions (more than one region per case possible): neurocranium (n = 42), neck (n = 13), lung and mediastinum (n = 24), abdomen (n = 181), and pelvis (n = 65). In 92.6 % of all cases (n = 253), image fusion was technically successful. Image fusion was able to improve sensitivity and specificity of the single modality, or to add important diagnostic information. Image fusion was problematic in cases of different body positions between the two imaging modalities or different positions of mobile organs. In 37.9 % of the cases, image fusion added clinically relevant information compared to the single modality. CONCLUSION: For clinical questions concerning liver, pancreas, rectum, neck, or neurocranium, image fusion is a reliable method suitable for routine clinical application. Organ motion still limits its feasibility and routine use in other areas (e. g., thorax).

Preoperative localization of parathyroid glands. Use of MRI, scintigraphy, and image fusion.

AIM: Minimally invasive resection of hyperfunctional parathyroid glands is an alternative to open surgery. However, it requires a precise preoperative localization. This study evaluated the diagnostic use of magnetic resonance (MR) imaging, parathyroid scintigraphy, and consecutive image fusion. PATIENTS, METHODS: 17 patients (9 women, 8 men; age: 29-72 years; mean: 51.2 years) with primary hyperparathyroidism were included. Examination by MRI used unenhanced T1- and T2-weighted sequences as well as contrast-enhanced T1-weighted sequences. (99m)Tc-MIBI scintigraphy consisted of planar and SPECT (single photon emission tomography) imaging techniques. In order to improve the anatomical localization of a scintigraphic focus, SPECT-data were fused with the corresponding MR-data using a modified version of the Express 5.0 software (Advanced Visual Systems, Waltham, MA). Results of image fusion were then compared to histopathology. RESULTS: In 14/17 patients, a single parathyroid adenoma was found. There were 3 cases with hyperplastic glands. MRI detected 10 (71%), scintigraphy 12 (86%) adenomas. Both modalities detected 1/3 patients with hyperplasia. Image fusion improved the anatomical assignment of the 13 scintigraphic foci in five patients and was helpful in the interpretation of inconclusive MR-findings in two patients. CONCLUSIONS: Both MRI and (99m)Tc-MIBI scintigraphy sensitively detect parathyroid adenomas but are less reliable in case of hyperplastic glands. In case of a scintigraphic focus, image fusion considerably improves its topographic assignment. Furthermore, it facilitates the evaluation of inconclusive MRI findings.

Diagnosis of neuroendocrine tumours by retrospective image fusion: is there a benefit?

This study evaluated the use of image fusion in the preoperative staging of neuroendocrine tumors (NET) of the pancreas and the gastrointestinal tract (GIT). Thirty-eight patients suffering from a metastasized NET with location of the primary in the pancreas ( n=15) or the GIT ( n=23) were examined by somatostatin receptor scintigraphy (SRS) and computed tomography (CT). Consecutive image registration and fusion were performed using custom-built software integrated in AVS/Express (Advanced Visual Systems, Waltham, MA, USA). Registration was performed by a voxel-based algorithm based on normalized mutual information. Image fusion was feasible in 36/38 patients. A total of 87 foci were assigned to anatomical regions (e.g. gut, pancreas, liver, lymph node or others) by two independent observers in both SRS and SRS/CT fusion images. The assignments used a binary ranking system (1="definite", 0="not definite"). These results were then retrospectively compared to the classification of the foci, based on postoperative histology or clinical follow-up. Imaging by SRS allowed a definite anatomical assignment in 57% (50/87) and 61% (53/87) of all lesions in the case of observers A and B, respectively. Image fusion improved the topographic assignment to 91% (79/87) and to 93% (81/87). The number classified as "definite" by both observers increased from 54% (47/87) to 86% (77/87). The increase in definite assignments was highly significant for both observers ( P<0.0001 for each). In the case of foci classified as liver metastases, image fusion allowed improved assignment to the corresponding liver segment from 45% (18/40) to 98% (39/40) and from 58% (23/40) to 100% (40/40) by observers A and B, respectively. Furthermore, the improved assignment of foci classified as lesions by image fusion was relevant for therapy in 7/36 patients (19%). Therefore, the image fusion technique presented herein appears to be a very useful method for clinical routine.

Sequence and analysis of chromosome 5 of the plant Arabidopsis thaliana.

The genome of the model plant Arabidopsis thaliana has been sequenced by an international collaboration, The Arabidopsis Genome Initiative. Here we report the complete sequence of chromosome 5. This chromosome is 26 megabases long; it is the second largest Arabidopsis chromosome and represents 21% of the sequenced regions of the genome. The sequence of chromosomes 2 and 4 have been reported previously and that of chromosomes 1 and 3, together with an analysis of the complete genome sequence, are reported in this issue. Analysis of the sequence of chromosome 5 yields further insights into centromere structure and the sequence determinants of heterochromatin condensation. The 5,874 genes encoded on chromosome 5 reveal several new functions in plants, and the patterns of gene organization provide insights into the mechanisms and extent of genome evolution in plants.

[Fusion of CT and PET data: methods and clinical relevance for planning laser-induced thermotherapy of liver metastases]. / Fusion von CT- und PET-Daten: Methode und klinische Bedeutung am Beispiel der Planung der laserinduzierten Thermotherapie von Lebermetastasen.

AIM: Catheter-placement in liver metastases is difficult when the lesions are not visible on plain images. We evaluated the use of image fusion using CT and PET data, providing information on anatomy and liver lesions, respectively. METHODS: Plain CT of the liver and whole body FDG-PET were performed in 28 patients with colorectal carcinoma in preparation for laser-induced thermotherapy. Fusions of image data and 2D-visualisation were performed and evaluated with regard to quality of the registration, number of detected lesions and influence of the procedure on laser-induced thermotherapy. Image fusion was performed using an algorithm which was developed by our group. RESULTS: 84 focal liver lesions were visible on CT, 107 on PET images. This means that CT guided catheter placement would have to be performed "blindly" in 23 lesions. In 6 patients previously unknown extra-hepatic tumor deposits were seen on PET images (local recurrence, 2 extrahepatic metastases, 2 second primaries). CONCLUSION: Hot spots from metastases, which are difficult to locate on PET images alone, may be identified on images generated by fusion of CT and PET data. These images facilitate an interventional approach to liver lesions which are not visible on plain CT. As PET allows for whole body screening, it helps identifying patients who would not profit from laser-induced thermotherapy.

Voxel similarity measures for 3-D serial MR brain image registration.

We have evaluated eight different similarity measures used for rigid body registration of serial magnetic resonance (MR) brain scans. To assess their accuracy we used 33 clinical three-dimensional (3-D) serial MR images, with deformable extradural tissue excluded by manual segmentation and simulated 3-D MR images with added intensity distortion. For each measure we determined the consistency of registration transformations for both sets of segmented and unsegmented data. We have shown that of the eight measures tested, the ones based on joint entropy produced the best consistency. In particular, these measures seemed to be least sensitive to the presence of extradural tissue. For these data the difference in accuracy of these joint entropy measures, with or without brain segmentation, was within the threshold of visually detectable change in the difference images.

Comparative genomic sequence analysis of the human and mouse cystic fibrosis transmembrane conductance regulator genes.

The identification of the cystic fibrosis transmembrane conductance regulator gene (CFTR) in 1989 represents a landmark accomplishment in human genetics. Since that time, there have been numerous advances in elucidating the function of the encoded protein and the physiological basis of cystic fibrosis. However, numerous areas of cystic fibrosis biology require additional investigation, some of which would be facilitated by information about the long-range sequence context of the CFTR gene. For example, the latter might provide clues about the sequence elements responsible for the temporal and spatial regulation of CFTR expression. We thus sought to establish the sequence of the chromosomal segments encompassing the human CFTR and mouse Cftr genes, with the hope of identifying conserved regions of biologic interest by sequence comparison. Bacterial clone-based physical maps of the relevant human and mouse genomic regions were constructed, and minimally overlapping sets of clones were selected and sequenced, eventually yielding approximately 1.6 Mb and approximately 358 kb of contiguous human and mouse sequence, respectively. These efforts have produced the complete sequence of the approximately 189-kb and approximately 152-kb segments containing the human CFTR and mouse Cftr genes, respectively, as well as significant amounts of flanking DNA. Analyses of the resulting data provide insights about the organization of the CFTR/Cftr genes and potential sequence elements regulating their expression. Furthermore, the generated sequence reveals the precise architecture of genes residing near CFTR/Cftr, including one known gene (WNT2/Wnt2) and two previously unknown genes that immediately flank CFTR/Cftr.

Registration of functional and anatomical MRI: accuracy assessment and application in navigated neurosurgery.

OBJECTIVE: A procedure for acquisition, automated registration, and fusion of functional and anatomical magnetic resonance images is presented. Its accuracy is quantitatively assessed using a publicly available gold standard. A patient case is used to illustrate the technique's clinical usefulness in image-guided neurosurgery. MATERIALS AND METHODS: Before and after functional MRI (fMRI) acquisition, additional anatomical images were acquired at spatial locations identical to those of the functional images (5-10 slices) for the purpose of voxel-based image registration. Registration accuracy of the anatomical volumes and high-resolution 3D MRI volumes (MP-RAGE imaging) was quantified using adapted data (8 patients) originating from the Vanderbilt Retrospective Registration Evaluation Project (NIH project 1 R01 NS33926-02). Selecting three subsets of slices from that data (5 slices/6 mm slice distance, 10 slices/3 mm distance, and 10 slices/6 mm distance), the small number of images available from fMRI acquisition was taken into account. Accuracies in registering these sparse data sets were then compared to the accuracy achieved using complete data. For clinical patient data (16 patients), fMRI images were fused with MP-RAGE images, thereby integrating anatomical images with information about the locations of functional areas. The resulting images were used for planning and navigation during tumor resections using an operating microscope (MKM, Zeiss). RESULTS: Quantitative analysis showed no loss of registration accuracy due to a reduced number of slices, regardless of whether 5 or 10 slices were used. For small-volume coverage in the anatomical images (thickness 24 mm), registration of one patient failed, and this could easily be identified by visual inspection. No failures were experienced when 54 mm was covered. In the clinical environment, all 16 interventions using fused fMRI and MRI data were successful. CONCLUSIONS: Automatic registration of functional and high-resolution anatomical MRI was found to be sufficiently accurate and reliable for use in stereotactic neurosurgery.

Generation and analysis of 280,000 human expressed sequence tags.

We report the generation of 319,311 single-pass sequencing reactions (known as expressed sequence tags, or ESTs) obtained from the 5' and 3' ends of 194,031 human cDNA clones. Our goal has been to obtain tag sequences from many different genes and to deposit these in the publicly accessible Data Base for Expressed Sequence Tags. Highly efficient automatic screening of the data allows deposition of the annotated sequences without delay. Sequences have been generated from 26 oligo(dT) primed directionally cloned libraries, of which 18 were normalized. The libraries were constructed using mRNA isolated from 17 different tissues representing three developmental states. Comparisons of a subset of our data with nonredundant human mRNA and protein data bases show that the ESTs represent many known sequences and contain many that are novel. Analysis of protein families using Hidden Markov Models confirms this observation and supports the contention that although normalization reduces significantly the relative abundance of redundant cDNA clones, it does not result in the complete removal of members of gene families.