Study of Urogenital Fistula in Rajshahi Medical College Hospital

This cross-sectional study was carried out in the gynae department of Rajshahi Medical College Hospital during the year January 2005 to December 2011, where 202 patients were studied for obstetric history, previous attempt at repair, the condition of the patient, the route of repair, age, etiological factors, techniques of surgery, socio-economic conditions, as well as treatment & results. Among 202 cases, surgery was done in 136 cases (67.33%). Among them 126 were successful (92.65%) and 10 failed (9.35%). Obstetric fistula results from obstructed labour occur mostly in the first pregnancy in young women. Prevention will include education, communication, transport, health care measures and prevention of early marriage. Awareness for mandatory hospital delivery in high risk pregnancies also can reduce obstetric fistulas. With good pre-operative intra-operative and post operative care and attention to surgical details may cure these affected woman.TAJ 2011; 24(2): 91-94

Ultrasound Image Segmentation: A Deeply Supervised Network With Attention to Boundaries

OBJECTIVE

Segmentation of anatomical structures in ultrasound images requires vast radiological knowledge and experience. Moreover, the manual segmentation often results in subjective variations, therefore, an automatic segmentation is desirable. We aim to develop a fully convolutional neural network (FCNN) with attentional deep supervision for the automatic and accurate segmentation of the ultrasound images.

METHOD

FCNN/CNNs are used to infer high-level context using low-level image features. In this paper, a sub-problem specific deep supervision of the FCNN is performed. The attention of fine resolution layers is steered to learn object boundary definitions using auxiliary losses, whereas coarse resolution layers are trained to discriminate object regions from the background. Furthermore, a customized scheme for downweighting the auxiliary losses and a trainable fusion layer are introduced. This produces an accurate segmentation and helps in dealing with the broken boundaries, usually found in the ultrasound images.

RESULTS

The proposed network is first tested for blood vessel segmentation in liver images. It results in F1 score, mean intersection over union, and dice index of 0.83, 0.83, and 0.79, respectively. The best values observed among the existing approaches are produced by U-net as 0.74, 0.81, and 0.75, respectively. The proposed network also results in dice index value of 0.91 in the lumen segmentation experiments on MICCAI 2011 IVUS challenge dataset, which is near to the provided reference value of 0.93. Furthermore, the improvements similar to vessel segmentation experiments are also observed in the experiment performed to segment lesions.

CONCLUSION

Deep supervision of the network based on the input-output characteristics of the layers results in improvement in overall segmentation accuracy.

SIGNIFICANCE

Sub-problem specific deep supervision for ultrasound image segmentation is the main contribution of this paper. Currently the network is trained and tested for fixed size inputs. It requires image resizing and limits the performance in small size images.

Assessing the correlation of genotypic and phenotypic responses of indica rice varieties under drought stress

Drought is one of the severe abiotic stress that affects the productivity of rice, an important staple crop that is consumed all over the world. The traits responsible for enhancing or adapting drought resistance in rice plants can be selected and studied to improve their growth under stress conditions. Experiments have been conducted on indica rice varieties comprising Sahabhagidhan as drought tolerant variety and IR64, MTU1010 categorized as drought sensitive varieties. Various root related biochemical and morphological traits such as root length, relative water content (RWC), xylem number, xylem area, proline content, and malondialdehyde content have been investigated for a comparative study of the plant response to drought stress in different rice varieties. The results of differential root transcriptome analysis have revealed that there is a notable difference in gene expression of OsPIP2;5 and OsNIP2;1 in various indica varieties of rice at different time periods of stress. The present work aims at assessing the correlation between genotypic and phenotypic traits that can contribute towards the emerging field of rice phenomics.